Microbiological Risk Analysis of Kungälv 

Drinking Water Treatment Plant 

Master of Science Thesis in the Master’s Programme Geo and Water Engineering 

FLORENCE ATUBO, MINA MAFINEJADASL 

Department of Civil and Environmental Engineering 

Division of Water Environment Technology 

CHALMERS UNIVERSITY OF TECHNOLOGY 

Gothenburg, Sweden, 2012 

Report No. 2012:142 

  



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Microbiological Risk Analysis of Kungälv 

Drinking Water Treatment Plant 

Master of Science Thesis in the Master’s Programme Geo and Water Engineering 

FLORENCE ATUBO, MINA MAFINEJADASL 

 

 

 

 

 

 

 

 

 

Department of Civil and Environmental Engineering 

Division of Water Environment Technology 

CHALMERS UNIVERSITY OF TECHNOLOGY 

Gothenburg, Sweden, 2012 

Report No. 2012:142 

 

  



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Microbiological Risk Analysis of Kungälv Drinking Water Treatment 

Plant 

Master of Science Thesis in the Master’s Programme Geo and Water Engineering 

FLORENCE ATUBO, MINA MAFINEJADASL 

 

 

 

Examensarbete/Institutionen för bygg-och miljöteknik 

Chalmers Tekniska Högskola 2012:142 

 

Department of Civil and Environmental Engineering 

Division of Water Environmental Technology 

Chalmers University of Technology 

SE-412 96 Göteborg 

Sweden 

Telephone:+46(0)31-7721000 

 

 

 

 

 

Cover: 

From left: View of Göta älv (Göta älv, u.d.), upper picture to the right: Combined Sewer Overflows (Daily 

Interesting Facts, 2012), lower picture: Adenovirus (ISIS INNOVATION, u.d.)  



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Acknowledgement 

We would like to express our sincere gratitude to our supervisors at Chalmers and Norconsult AB. 

We would like to thank Assistant Professor Thomas Pettersson at Chalmers University of 

Technology, at the division Water Environment Technology at the department Civil and 

Environmental Engineering for his faithful supervision and support with precious material and advice 

throughout this master thesis. We are also grateful to Madeleine Foss and Alexander Olsson at 

Norconsult AB for the helpful material they made available to us and the insightful advice from the 

discussions held with them.  Great thanks to Adjunct Professor Olof Bergstedt at Gothenburg water 

division for providing us valuable data to carry out the study. We would also like to express our most 

sincere gratitude to Jaya Hoy for providing us free access to Analytica (Risk and Decision Analysis 

Software) for the study. Special thanks go to Geoff Sayer for his help in editing this document.  

Thank you all very much!! 

  



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Microbiological Risk Analysis of Kungälv Drinking Water Treatment Plant 

Master of Science Thesis in the Master’s Programme Geo and Water Engineering 

FLORENCE ATUBO, MINA MAFINEJADASL 

Department of Civil and Environmental Engineering 

Division of Water Environment Technology 

CHALMERS UNIVERSITY OF TECHNOLOGY 

Abstract 

Human activities are changing the world’s climate by increasing energy-trapping gases responsible 

for the “green-house” effect. The world’s average temperature has increased by approximately 0.6
o
C 

and approximately two thirds of that warming has occurred since 1975. Further warming is forecast 

together with changes in precipitation and climatic variability in the coming century. Temperatures 

are expected to increase more in Sweden and Scandinavia than the global mean. Changes in 

temperature and runoff will have an effect on quality of raw water sources for drinking water. Humus 

levels, algal bloom and microbial contamination will increase in raw water sources as a result and 

present-day drinking water treatment processes will be inadequate in achieving the recommended 

health targets, causing waterborne disease outbreaks. In order to prepare for the predicted 

precipitation increase due to climate change and its effects on raw water sources, the municipality of 

Kungälv, has decided to design a new treatment plant carried out by Norconsult AB.  

The main aim of the study is to assess whether the proposed drinking water treatment plant will be 

effective in removing pathogens to a level that fulfils the USEPA guideline value. The study 

investigated the health risks that the consumers in Kungälv are exposed to today and in the future by 

studying the operation and reliability of the processes in the proposed treatment plant, and their 

removal efficiency of pathogens. The health risks were calculated through literature reviews and by 

application of the Quantitative Microbiological Risk Assessment tool developed by the Swedish 

Water and Wastewater Association. Risk analyses were conducted in three scenarios: for present 

conditions, future up to 2060 and the case of a waterborne disease outbreak upstream the raw water 

intake for Kungälv.  

The result showed that the population of Kungälv connected to the current drinking water treatment 

plant seem to be very vulnerable to waterborne disease infection since the model indicates no 

effective pathogen removal. The annual risks of infection for all pathogens except Salmonella were 

above the USEPA guideline values. This demonstrates the importance of the proposed treatment 

plant, which will provide better pathogen removal and inactivation barriers. However, the study also 

shows that while being effective in removal of other pathogens, the proposed plant is not capable of 

fully removing Adenoviruses without the using chlorination. To be able to achieve safe drinking 

water with the proposed DWTP, chlorination is recommended to be used all year round to reduce the 

infection risk today as well as in the future (2060). The study also recommends that sources of 

contamination like wastewater from combined system overflows be directed to wastewater treatment 

systems like treatment ponds in order to reduce the pathogen loads to the raw water. 

Key words: Pathogen concentration, QMRA, Adenovirus, Climate change, Drinking Water 

Treatment Plant, Kungälv, Göta älv, Risk of infection, Risk analysis 



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Mikrobiologisk Riskanalys av Kungälvs dricksvatten reningsverk 

Examensarbete inom masterprogrammet Geo and Water Engineering 

FLORENCE ATUBO, MINA MAFINEJADASL  

Institutionen för bygg-och miljöteknik 

Avdelningen för Vatten Miljö Teknik 

Chalmers Tekniska Högskola 

Sammanfattning 

Mänskliga aktiviteter förändrar jordens klimat genom ökade utsläpp av olika gaser som bidrar till 

växthuseffekten. Världens medeltemperatur har ökat med cirka 0,6
o
C och ungefär två tredjedelar av 

uppvärmningen har skett sedan 1975. Under det kommande århundradet förväntas en ytterligare 

uppvärmning tillsammans med förändringar i nederbörd och klimatvariationer. Temperaturen väntas i 

medeltal öka mer i Sverige och Skandinavien än t globalt. Förändringar i temperatur och 

markavrinning kommer att ha effekt på kvaliteten i dricksvattentäkter. Humushalter, algblomning och 

mikrobiella föroreningar förväntas  öka i råvattenkällor som ett resultat och dagens 

behandlingsprocesser för att bereda dricksvatten kommer att vara otillräcklig för att uppnå de 

rekommenderade hälsokraven vilket upphov till vattenburna sjukdomsutbrott. Kungälvs kommun har 

beslutat med Norconsult som projectör att bygga ett nytt dricksvattenreningsverk på grund av 

förutspådda klikatförändringama och dess effekter på råvattenttäkter. 

Syftet med studien var att bedöma om det föreslagna vattenverket kommer att vara effektivt 

tillräckligt för att avlägsna patogener till den nivå som uppfyller den amerikanska 

miljövårdsmyndighetens (USEPA) riktvärde. I studien undersöktes de hälsorisker som 

konsumenterna i Kungälv utsätts för idag och i framtiden genom att studera funktionen och 

tillförlitligheten av de processerna i det föreslagna vattenverket och reningseffektivitet av patogener. 

Hälsorisker beräknades med hjälp av kvantitativ mikrobiell riskanalys (QMRA) verktyg som 

utvecklats av Svenskt Vatten.  Riskanalyser utfördes utifrån  scenarier för nuvarande och en framtida 

situation samt för ett vattenburet sjukdomsutbrott uppströms råvattenintaget vid  Kungälvs 

vattenverk. 

Resultatet visade att det finns risker för befolkningen i Kungälv som är ansluten till detbefintliga 

vattenverket att de drabbas av infektion eftersom det nuvarande vattenverket inte är tillräckligt 

effektivt att avlägsna patogener. De årliga riskerna för infektion för alla patogener utöver salmonella 

låg över den amerikanska miljövårdsmyndighetens (USEPA) riktvärden för det nuvarande systemet. 

Det är därför viktigt att uppgradera det nuvarande vattenverket med det föreslagna processerna som 

har bättre patogeninaktivering/reducering. Men studien visar också att vattenverket inte 

avskilja/inaktivera Adenovirus tillräckligt utan att använd klorering. För att kunna leverera ett rent 

dricksvatten med det föreslagna vattenverket, rekommenderas användning av klorering året runt för 

att minska infektionsrisken redan idag och i framtiden (2060). Studien rekommenderar också att 

åtgärder vidtas föratt minska effekterna från föroreningskällor såsom bräddning av avloppsvatten 

med  utsläpp  patogena ämnena till råvattentäkten. 

Nyckelord: Patogen halter, QMRA, Adenovirus, Klimat förändring, Dricksvatten reningsverk, 

Kungälv, Göta älv, Infektionsrisk, Riskanalys  



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List of Tables 

Table 1. Waterborne pathogens and their significance a in water supply (WHO, 2008) ................................... 12 

Table 2. Virus types common in Sweden and the infection caused ................................................................... 14 

Table 3. Removal efficiency (log10-reduction) for the treatment processes studied and other input data in the 

MRA model ....................................................................................................................................................... 20 

Table 4. Content of pathogens and indicator organism E.coli in faecal material of infected persons ............... 21 

Table 5. Measured median concentration of E.coli in Göta älv ......................................................................... 23 

Table 6. Average concentration of pathogens in Göta älv ................................................................................. 23 

Table 7. Estimated average pathogen concentration 2060 ................................................................................. 26 

Table 8. Log10- reduction in case of one-meter groundwater level rising in the infiltration basins ................... 27 

Table 9. Average concentration of pathogens in the raw water source considering an outbreak upstream the 

DWTP of Kungälv ............................................................................................................................................. 28 

Table 10. Annual risk of infection of the current DWTP in Kungälv ................................................................ 29 

Table 11. Annual risk of infection with and without chlorination added to the proposed treatment system ..... 32 

Table 12.  Infection risk when a treatment barrier is removed from the system considering the measured & 

estimated concentrations .................................................................................................................................... 32 

Table 13. Infection risk when a treatment barrier is removed from the system considering the standard 

concentrations .................................................................................................................................................... 33 

Table 14. Annual infection risk when two UV units are out of function from the treatment system considering 

the measured/estimated concentrations .............................................................................................................. 34 

Table 15. Annual infection risk when two UV units are out of function from the treatment system considering 

the standard concentrations ................................................................................................................................ 34 

Table 16. Annual infection risk for the year 2060 results .................................................................................. 35 

Table 17. Annual infection risk in case of disease outbreak (with and without chlorination in the system) ..... 36 

Table 18.  Annual infection risk for a disease outbreak (Adenovirus) concentration using 30 sand filters ....... 36 

Table 19. The Swedish limit value used to categorize water into suitable or unsuitable drinking water 

(Livsmedelsverkets författningssamling, 2011) ................................................................................................. xiv 

Table 20. Pathogens of concern to water suppliers (WHO, 2008) ...................................................................... xv 

Table 21.  Different viruses identified in faeces of healthy and infected humans found in the raw sewage 

(Joseph, et al., 1978) .......................................................................................................................................... xvi 

Table 22. Removal efficiency (log10-reduction) in slow sand filtration for pathogens ..................................... xvii 

Table 23. Estimated removal efficiency for the infiltration basins in Kungälv AR-plant ............................... xviii 

Table 24.  Daily and annual risk of the DWTP currently used ........................................................................ xviii 

Table 25. Calculated bacteria and virus concentrations in Göta älv ................................................................... xx 

Table 26. Measured Cryptosporidium and Giardia in Göta älv ( (Bergstedt, 2012) ........................................... xx 

Table 27.  Log10- reduction and infection risk without chlorination (with measured and standard 

concentrations) ................................................................................................................................................... xxi 

Table 28. Daily and annual infection risk with standard concentrations ........................................................... xxi 

Table 29. Daily infection risks with chlorination ............................................................................................. xxiii 

Table 30. Daily infection risk when a barrier is closed down considering the measured/estimated 

concentrations .................................................................................................................................................. xxiii 

Table 31. Daily infection risk when a barrier is closed down considering standard concentrations ............... xxiv 

Table 32. Infection risk when 2 UV units are functional considering measured/estimated concentrations..... xxiv 

Table 33. Infection risk when 2 UV units are functional considering standard concentrations ....................... xxiv 

Table 34. Estimated pathogens concentrations from year 2010 to 2060........................................................... xxv 

Table 35. Daily and annual infection risk for year 2060 .................................................................................. xxvi 



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Table 36. Daily and annual infection risk for year 2060 in case of raised groundwater table by one-metre ... xxvi 

Table 37. Concentrations of pathogens due to an outbreak upstream the city of Kungälv ............................. xxvii 

Table 38. Infection risks during an outbreak and there is no chlorination ...................................................... xxvii 

Table 39. Infection risks during an outbreak and there is with chlorination ...................................................xxviii 

  



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List of Figures 

Figure 1. Municipalities of Kungälv, Ale, Stenungsund and Tjörn (Google earth, u.d.) ......................................3 

Figure 2. Conceptual model of pathogen flow into the surface water due to increased precipitation – adapted 

from a blog of school of Peniche (Anon., 2008) ...................................................................................................4 

Figure 3. Microbial barriers efficiency (Engblom & Lundh, 2006) ......................................................................6 

Figure 4. Dösebacka AR-plant (Google earth, u.d.)..............................................................................................7 

Figure 5. Process diagram of current treatment plant (EU, 2002) ........................................................................8 

Figure 6. Conceptual Model of the proposed water treatment plant (Norconsult, 2011) ......................................9 

Figure 7. Framework for the Microbial Risk Assessment in four steps (Medema & Ashbolt, 2006) ............... 19 

Figure 8. Trend of pathogen concentrations in Göta älv from 2010 to 2060 ..................................................... 26 

Figure 9. The number of infected people from the current treatment plant ....................................................... 30 

Figure 10. Log10–reduction by the different barriers in the proposed drinking water treatment plant............... 31 

Figure 11. Number of people infected if treatment process is removed ............................................................ 33 

Figure 12. Annual infection risks of the scenario cases studied against the USEPA guideline value (1 x 10
-4

) 37 

Figure 13.  The Dösebacka infiltration basins and sedimentation basin (picture to the left (Google earth, u.d.)) 

and intake wells (picture to the right (Zagerholm, et al., 2007)) ........................................................................ xiv 

Figure 14. Artificial Infiltration basins (UNEP, 2012) ....................................................................................... xv 

Figure 15. Triangular probability distribution (minimum, mode, maximum) .................................................. xvii 

Figure 16. Gamma probability distribution (MathWorks, 2012) ....................................................................... xix 

Figure 17. Schematic figure of reservoirs and the direction of the inlet and out let flows ............................... xxii 

 

  

file:///D:/qmra-report0507.docx%23_Toc332564109


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Acronyms 

 

AWWA - American Water Works Association 

CSOs -Combined Sewer Overflows 

DALY- Disability Adjusted Life Years  

DNA - Deoxyribonucleic acid 

DWTP - Drinking Water Treatment Plant 

EHEC - Enterohaemorrhagic Escherischia coli  

IPCC- Intergovernmental Panel on Climate Change  

NFA-National Food Administration  

PAC - Polyaluminum Chloride  

QMRA- Quantitative Microbial Risk Assessment 

RNA - Ribonucleic acid 

SCCV - Swedish Commission on Climate and Vulnerability 

SDWF - Safe Drinking Water Foundation 

SVU- Svensk Vatten Utveckling 

SWWA - The Swedish Water and Wastewater Association 

UN - United Nations 

USEPA -United States Environmental Protection Agency 

UV – Ultra Violet 

WWTP - Waste Water Treatment Plant 

  



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Table of Contents 
Acknowledgement ................................................................................................................................................ v 

Abstract ................................................................................................................................................................ vi 

Sammanfattning .................................................................................................................................................. vii 

List of Tables ..................................................................................................................................................... viii 

List of Figures ....................................................................................................................................................... x 

Acronyms ............................................................................................................................................................. xi 

1 Introduction ........................................................................................................................................................1 

1.1 Problem Statement ......................................................................................................................................3 

1.2 Aim and objectives ................................................................................................................................4 

1.3 Scope ...........................................................................................................................................................4 

2. Drinking Water Treatment ................................................................................................................................6 

2.1 Kungälv’s Current Drinking Water Treatment Plant Today .......................................................................6 

2.2 The New Proposed Drinking Water Treatment Plant .................................................................................8 

2.2.1. New surface water treatment plant ......................................................................................................9 

2.2.2. Groundwater treatment plant............................................................................................................ 10 

3. Microorganisms ............................................................................................................................................. 12 

3.1 Waterborne pathogenic microorganisms .................................................................................................. 12 

3.1.1 Bacteria ............................................................................................................................................. 12 

3.1.2 Viruses .............................................................................................................................................. 13 

3.1.3 Protozoa ............................................................................................................................................ 15 

3.2 Faecal indicator organisms ....................................................................................................................... 15 

3.3 Water quality and health target ................................................................................................................ 16 

4. Material and Methods .................................................................................................................................... 18 

4.1 QMRA methodology ................................................................................................................................ 18 

4.1.1 QMRA input data .............................................................................................................................. 19 

4.2 Estimation of annual infection risk .......................................................................................................... 21 

4.3 Current DWTP ......................................................................................................................................... 22 

4.4 Scenario1 (Risks of infection - proposed treatment system) .................................................................... 22 

4.4.1 Pathogen concentration in Göta älv .................................................................................................. 22 

4.4.2 Case 1- Annual infection risk without chlorination .......................................................................... 23 

4.4.3 Case 2- Annual infection risk with chlorination ............................................................................... 24 

4.4.4 Case 3- Annual infection risk when one of the barriers is removed from the treatment plant .......... 24 

4.4.5 Case 4- Annual infection risk during maintenance of UV-disinfection units ................................... 24 

4.5. Scenario2 (Annual infection risk in 2060) .............................................................................................. 25 



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4.5.1 Pathogen concentration estimation ................................................................................................... 25 

4.5.2. MRA model input data ..................................................................................................................... 26 

4.5.3. Estimation of annual infection risk .................................................................................................. 26 

4.5.4. Pathogen removal efficiency by infiltration basins in 2060 ............................................................. 26 

4.6. Scenario 3- Waterborne outbreak in upstream town ............................................................................... 27 

4.6.1 Estimation of annual infection risk ................................................................................................... 28 

5. Results ............................................................................................................................................................ 29 

5.1. Annual infection (treatment plant currently used) .................................................................................. 29 

5.2 Scenario1 (Infection risk under today’s conditions) ................................................................................ 30 

5.2.1 Case 1- Annual infection risk without chlorination .......................................................................... 30 

5.2.2 Case 2-Annual infection risk with chlorination ................................................................................ 31 

5.2.3 Case 3-Annual infection risk when one barrier is removed from the treatment plant ...................... 32 

5.2.4 Case 4-Annual infection risk with inactivation of 2 UV-units .......................................................... 34 

5.3. Scenario 2 (Infection risk in 2060).......................................................................................................... 34 

5.3.1 Annual risk of infection for 2060 ...................................................................................................... 34 

5.3.2 Removal efficiency if the unsaturated zone in the infiltration basins changes (2060) ...................... 35 

5.4. Scenario 3 (Infection risk if there is a waterborne disease outbreak upstream of raw water source) ..... 35 

6. Discussion and recommendations .................................................................................................................. 38 

6.1 Scenario1- Proposed treatment plant with today’s condition ................................................................... 38 

6.2 Scenario 2-Risk of infection in year 2060 for the proposed treatment system ........................................ 39 

6.3 Scenario 3- Infection risk due to an outbreak upstream of Kungälv ........................................................ 40 

6.4 Recommendations .................................................................................................................................... 40 

7. Conclusions .................................................................................................................................................... 41 

References ............................................................................................................................................................ ix 

APPENDICES ................................................................................................................................................... xiv 

Appendix 1 ......................................................................................................................................................... xiv 

Appendix 2: Scenario1 (year 2011) ................................................................................................................... xix 

Appendix 3: Scenario 2 (year 2060) ................................................................................................................. xxv 

Appendix 4: Scenario 3-Waterborn outbreak upstream of Kungälv ............................................................... xxvii 

 

  



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1 Introduction 

The world’s climate system is fundamental to the stability of ecosystems and human societies that 

depend on it. Today however, human activities are changing the world’s climate by increasing energy-

trapping gases responsible for the “green-house” effect. These gases comprise mainly carbon dioxide 

(from fossil fuel combustion and forest burning), methane (from agriculture, animal husbandry and oil 

extraction), nitrous oxides and manmade halocarbons (WHO, 2003). 

The UN’s Intergovernmental Panel on Climate Change (IPCC) in its third report stated that "There is 

new and stronger evidence that most of the warming observed over the last 50 years is attributable to 

human activities." During the twentieth century, the world’s average temperature has increased by 

approximately 0.6 
o
C and approximately two thirds of that warming has occurred since 1975. Further 

warming is forecast together with climatic variability including precipitation in the coming century, and 

these forecasts are based on sophisticated global climate models (WHO, 2003) 

General mean annual precipitation is projected to increase in northern Europe and decrease in the south. 

The changes in precipitation are also predicted to vary from season to season and across regions 

according to changes in large-scale circulation and water vapour loading. Annual runoff is projected to 

increase in northern Europe by approximately 5-15% up to the 2020’s and by 9-22% up to the 2070’s. 

These precipitation changes will have a range of impacts on water resources especially risks that arise 

from floods (IPPC, 2008). 

Sweden is not exempt from these changes. Temperatures are expected to rise more in Sweden and 

Scandinavia than the global mean. There has been significant warming in Sweden since the late 1980s 

and the past 15-20 years have been distinctly warm where for example the average temperature in winter 

has been 1 
o
C higher than a hundred years ago. The difference in winter temperature between 1961-1990 

and 1991-2005 is around 2
 o
C (SCCV, 2007). 

From hydrological calculations, annual runoff will increase over greater parts of Sweden particularly in 

Norrland and in western Götaland. In parts of the country where precipitation is expected to increase, 

floods are likely to happen and this may affect the water supply. These increases in temperature and 

runoff will have impacts on both inland and sea waters. At the moment Sweden has access to large 

quantities of good quality water but these changes in temperature and runoff will have an effect on the 

quality of raw water sources for drinking water. Humus levels, algal bloom and microbial contamination 

will increase as a result and present-day treatment processes will be inadequate (SCCV, 2007). The 

increased risk of floods and landslides may also mean that pollution from contaminated soil and old 

landfills can be spread (Regeringskansliet, 2009). 

Half of Sweden’s water supply comes from surface water (lakes, rivers and streams) and the other half 

from groundwater which is mainly infiltrated surface water. Because of the comparatively good quality 

of raw water from these sources, the treatment techniques in Sweden are relatively simple. Treatment 

plants that use groundwater as raw water often use simpler techniques than surface water treatment 

plants. The common processes as microbial barriers in a surface water treatment plant are chemical 

precipitation including filtration and chlorination as a disinfection process (SCCV, 2007).   



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There is need for adaptation to the predicted climate change and the water management challenges that 

come with it. For Sweden, this is particularly concerning the risk of flooding. Strategies for reducing 

vulnerability to climate change must be coupled with efforts to cut climate emissions. At national levels, 

construction of reservoirs, dykes and flood plains are the main measures to protect against flooding 

(IPPC, 2008). Complementary strategies for water treatment plants would require design of efficient 

water treatment technologies to cope with the expected change in water quality.  

Good quality water supply is a prerequisite for modern life and local authorities are responsible for 

water and sewage in the urban areas. Sweden has approximately 2000 publicly owned water works 

serving a population of about 7.7 million (82% of the current population), 10% of which use surface 

water as raw water sources. The surface water works serve 51% of this population; ground water serves 

23% and artificial groundwater (infiltrated surface water) plants serve 26%. In Sweden, although quality 

of drinking water is the responsibility of the Ministry of Agriculture with the National Food 

Administration as the central supervising agency, water supply and sanitation management is the duty of 

the local government or municipality (SWWA, 2000). This implies that the preparations for adapting to 

climate change are the responsibility of the municipality where the water supply plants are located.  

The Göta älv which is the source for Kungälv is considered of poor quality by Swedish standards. The 

municipality of Kungälv currently supplies water to consumers taken through a very simple drinking 

water treatment system (see chapter 2) but in order to prepare for the predicted increase in precipitation 

due to climate change, a new treatment plant has been proposed. 

Study area 

Kungälv municipality covers an area of 364 square kilometres in Västra Götaland, Sweden and is 

bordered by Gothenburg, Stenungsund, Tjörn and Ale municipalities. The current population of 

Kungälv is 41 473 inhabitants and is predicted to increase to 64 600 by year 2035 (Kungälvs Kommun, 

2011) and probably 90 600 inhabitants by 2060 if the population prediction factors remain the same. 

Drinking water in Kungälv has to meet the quality requirements of the National Food Administration. 

To ensure that this quality is achieved, the municipality has three water treatment plants: Dösebacka, 

Lysegården and Marstrand. These plants supply water to about 25 000 people with an approximate 

consumption of 0.2 m
3
/p*day (200 l/p*day).  The raw water source for the treatment plants is River 

Göta Älv although they have different treatment methods. Dösebacka and Lysegården plants treat water 

through artificial recharger infiltration and thereafter only pH-adjustment is performed while Marstrand 

is a surface water treatment plant with chemical treatment and sand filtration (Kungälvs Kommun, 

2011).  

The raw water in Kungälv has today seemed to be of sufficient quality to require only basic treatment 

but in preparation for future climate change and its impacts, a new treatment plant has been proposed for 

Dösebacka. The proposed plant will supply water to Kungälv, Ale, Stenungsund and Tjörn 

municipalities (Figure 1). 



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Figure 1. Municipalities of Kungälv, Ale, Stenungsund and Tjörn (Google earth, u.d.) 

1.1 Problem Statement 

Climate change scenarios do show an increase in temperature and precipitation, which results in 

increased water runoff into surface and underground water sources. Greater runoff leads to the surface 

water increase, hence increasing the humic acid levels, algal bloom and microbial contamination that 

negatively affect the water quality. Higher levels of precipitation are likely to increase agricultural 

runoff, flows of urban stormwater and sanitary sewage to drainage basins (Figure 2). The River Göta 

Älv which is a source of raw water for five drinking water treatment plants has been identified as an 

area of concern with risks of floods, landslides and dam failures (Karlsson, 2009). Concentrations of 

indicator bacteria Escherichia coli have been detected in Göta Älv at levels as high as 2800 CFU/100ml 

in 2006, which is more than five times the Swedish guideline value of 500 CFU/100ml (Åström, 2007). 

The presence of E.coli demonstrates the presence of faecal pollution and possible pathogens. 

Consumption of poor water quality may result in serious illness due to pathogens like bacteria, viruses 

and protozoa (Figueras & Borrego, 2010).  

The Swedish National Food Administration states that between 1 and 13 waterborne outbreaks have 

been reported annually in Sweden with infection from various pathogens ranging from 100 to over 10 

000 individuals.  The annual infection risk from waterborne outbreaks has been estimated to be 1 in 10 

000 in Sweden (Lindberg & Lindqvist, 2005).  A study performed by Svensk Vatten Utveckling (SVU) 

for Göta Älv shows that the microbial load in the river water is significantly affected by the discharges 

of wastewater such as combined sewer overflows –‘CSOs’, emergency discharges and wastewater 

treatment plant effluents (Åström & Pettersson, 2007). 

In order to prevent waterborne outbreaks, drinking water must be effectively treated. The current 

drinking water treatment system used in Kungälv whose treatment barriers include sedimentation and 

infiltration may not efficiently remove pathogens if the water quality deteriorates further. This impelled 

the design of a new drinking water treatment plant (DWTP) to meet the challenge of varying water 



4 
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quality both at present and in the future. The proposed plant should have efficient treatment processes to 

ensure that drinking water quality is safe for the consumers. 

 

 
Figure 2. Conceptual model of pathogen flow into the surface water due to increased precipitation – adapted from a 

blog of school of Peniche (Anon., 2008) 

1.2 Aim and objectives 

The main aim of the study is to assess whether the proposed drinking water treatment plant designed by 

Norconsult is effective in removing pathogens to a level that fulfils the USEPA guideline value. Areas 

of improvement in the proposed DWTP will also be recommended. The study investigates the health 

risks that the consumers in Kungälv are exposed to today and in the future by studying the operation and 

reliability of the processes in the proposed treatment plant, and their removal efficiency of pathogens. 

Scenarios with pathogens discharged into the raw water source for the proposed water treatment plant 

are used to determine the safety of the treatment processes. These scenarios include functioning of the 

system for the conditions of today, pathogen loading in the future (up to 2060) and determine the  

loading that would cause waterborne disease outbreaks.  

The proposed treatment plant is tested for its capability (removal efficiency) in removing pathogens in 

order to fulfil prescribed health targets (Appendix 1). It is recommended that the plant should achieve a 

health based target in which no more than one person in a population of 10 000 becomes infected 

annually (       ) (Signor & Ashbolt, 2009).   

1.3 Scope 

This study is conducted for the Kungälv municipality and is a microbiological risk analysis performed 

on the current and a new proposed treatment plants for Kungälv, using the River Göta Älv as the raw 

water source. Traditionally, faecal pollution indicator microorganisms have been used to estimate the 



5 
` 

presence of pathogens in drinking water. However, cases have been found where the pathogens have 

existed in the absence of indicator organisms (Figueras & Borrego, 2010). This governed our study of 

the treatment system to focus on the pathogens normally found in Sweden (River Göta Älv) instead of 

using indicator organisms only. The pathogens studied are in three categories: viruses (Adenovirus, 

Norovirus, and Rotavirus), bacteria (Campylobacter, Salmonella, Enterohaemorrhagic Escherischia coli -

EHEC) and protozoa (Cryptosporidium, Giardia).  

The investigative study was carried out using the Swedish Quantitative Microbiological Risk 

Assessment (QMRA) tool developed by the Swedish Water & Wastewater Association that determines 

health risks to drinking water consumers (Svensk Vatten Utveckling, 2012). The newly proposed 

drinking water treatment system has several processes (section 2.2), which have been evaluated using 

the QMRA model and include: direct filtration (DynaSand filters), infiltration basins, rapid sand 

filtration, UV-disinfection and chlorination during emergency situations.  

  



6 
` 

2. Drinking Water Treatment 

This study focused on the pathogenic microorganisms in raw water and the treatment processes that may 

be used to remove or inactivate them in order to meet water quality guidelines and targets. Surface water 

in Sweden naturally contains more humus than groundwater. Therefore it contains more organic 

particles and, microorganisms and is likely to exhibit variations in quality. Consequently more efficient 

and robust microbial barriers are needed for drinking water treatment plants (DWTP) with surface water 

as the raw water source (Lindberg & Lindqvist, 2005).  The efficiency of a microbial barrier depends on 

two factors: removal and inactivation and in drinking water production it is preferable that both factors 

are considered (Figure 3). The reduction achieved by the barrier is expressed as a logarithmic equation 

(Engblom & Lundh, 2006). Log10- reduction shows the relative number of microbes eliminated by a 

treatment processes. One log10 reduction means that the pathogens are reduced 10 times (pathogens 

decrease by 90%) (Healthy Facilities Institute, 2012).  

                     
 

  
                                                                                                                            (1) 

                                                           

                                                              

 

Figure 3. Microbial barriers efficiency (Engblom & Lundh, 2006) 

A treatment train involving chemical precipitation and filtration (slow sand filtration and /or rapid sand 

filtration where rapid sand filtration is much more common than slow sand filtration) is traditionally 

performed in Sweden due to high content of natural organic matter in surface water. Effective pathogen 

removal minimizes the need for disinfection. For groundwater, disinfection is often not used. Swedish 

DWTPs follow a new trend where less disinfection is used, especially disinfection with chlorine, even 

though the dosage is low in relation to the levels applied internationally. Instead, UV-light has replaced 

chlorine disinfection (Lindberg & Lindqvist, 2005). 

2.1 Kungälv’s Current Drinking Water Treatment Plant Today 

Dösebacka is an Artificial Groundwater Recharge plant (AR-plant) that is situated along the western 

side of the river Göta Älv, 5 km north of Kungälv city (Figure 4).  Dösebacka AR-plant supplies the 



7 
` 

population with 2.2 million cubic metres of drinking water annually (approximately 6 000 m
3
/day) 

mainly to Kungälv and some parts of Ale municipality (Zagerholm, et al., 2007).  

 

Figure 4. Dösebacka AR-plant (Google earth, u.d.) 

The existing plant consists of one sedimentation basin, 9 infiltration basins and 15 abstraction wells 

(Appendix 1) shown in Figure 5. The treatment process begins with intake of surface water from Göta 

älv pumped to a sedimentation basin and conveyed to infiltration basins. The filtered water is then 

abstracted through wells and pumped to two reservoirs. Water abstracted in two wells exhibits very high 

turbidity and undergoes additional treatment by chemical precipitation using aluminium sulphate and 

filtration.  Before the distribution from the reservoirs, the water pH is adjusted to prevent pipe corrosion. 

The treated water shows low microbial content, but uses chlorination to treat the water when necessary 

(EU, 2002). 



8 
` 

 

Figure 5. Process diagram of current treatment plant (EU, 2002) 

Sedimentation Basin 

Sedimentation basins are large tanks where water flows slowly to promote the sedimentation of 

particles.  

Infiltration basins 

In infiltration basins, water is percolated over a period of days through biologically and chemically 

active soil environment (Appendix 1, Figure 14). The soil environment reduces nutrient loads in water 

through nitrification/denitrification reactions, adsorption reactions to reduce phosphorus and filtration to 

remove suspended particles (Sumner & Bradner, 1996). The unsaturated zone is the most effective in 

the treatment of the water and Dösebacka unsaturated zone varies between 0 to 7.7 m (EU, 2002). The 

infiltration basins have a total area of 12 000 m
2
 and have an elevation 2 to 3 meters higher than the 

ground level at the abstraction wells that are located 100 to 150 meters from the basins (Zagerholm, et 

al., 2007).  

The infiltrated water is received in abstraction wells that are placed as close as possible to the Göta älv 

shore in order for the water to spend longer duration in the aquifer. According to Zagerholm et al, the 

average duration of water in the aquifer (from dam F to the intake well GRP9) is approximately 250 

hours or 10 days (Zagerholm, et al., 2007). 

Sand Filters 

Sand filters contain porous media through which water passes and particulate matter captured (SDWF, 

2012).  The water from abstraction wells number 9 and 11 in Dösebacka is turbid and therefore 

aluminium sulphate is added to reduce the turbidity through coagulation and flocculation. The flocs 

formed and other suspended particles are then removed by sand filtration. 

2.2 The New Proposed Drinking Water Treatment Plant 

The proposed treatment plant for Kungälv municipality designed by Norconsult consists of today’s 

artificial recharge plant upgraded with surface water and groundwater treatment processes. The 

treatment plant is categorised as a surface water treatment plant and a groundwater treatment plant. The 



9 
` 

surface water treatment processes include screening, micro-straining, chemical precipitation, rapid 

filtration (DynaSand filters) and carbon filtration to reduce the amount of suspended particles e.g. 

natural organic matter (NOM) and increase the infiltration capacity. The groundwater treatment 

processes include infiltration followed by oxidation, rapid sand filtration and UV-disinfection. These 

processes are planned to fulfil the health based targets in regarding waterborne pathogens.  The 

proposed treatment system is conceptually illustrated (Figure 6) below.  

 

Figure 6. Conceptual Model of the proposed water treatment plant (Norconsult, 2011) 

 

2.2.1. New surface water treatment plant 

Screening and micro- straining 

Two screens are installed at the intake at Göta älv to prevent debris and large particles from reaching the 

intake pipes and the pumping station (capacity of 830m
3
/h). Water is extracted at a flow rate of 0.2m

3
/s. 

The first treatment barrier after abstraction is micro-straining and the strainers have a pore size of 

approximately 60 µm.  The purpose of micro-strainers is to remove algal cells, fractions of protozoa and 

reduce the quantity of coagulant required for further removal of suspended materials (LeChevallier & 

Keung Au, 2004).  



10 
` 

Pre-alkalinisation and chemical precipitation 

After micro straining, pre-alkalinisation follows, here chalk or lime is used to adjust the total hardness, 

total alkalinity and pH, to act as a buffer to keep the pH constant. This is followed by chemical 

precipitation where dissolved NOM and (ionic) metals are removed by conversion to insoluble particles 

by chemical reaction (Water Specialists Technologies, 2009). The chemical used in this process is 

Polyaluminum Chloride (PAC) which is added at the water inflow to the rapid filters. Chemical 

precipitation for the proposed treatment system refers only to coagulation which promotes the 

interaction of small particles to form larger particles known as flocs (LeChevallier & Keung Au, 2004).  

Rapid Filtration (DynaSand filters) 

Filtration is the process of removing suspended solids from water passing through a permeable 

membrane or porous bed of materials. Rapid sand filters (DynaSand type) are the proposed filtration 

type for this system. The precipitation and aggregation of particles occurs in the filtration beds where 

the flocs are removed. This filtration is therefore a type of conventional treatment called direct filtration 

(preceded by chemical coagulation) or in-line filtration (Smeets, et al., 2006). After filtration water 

passes by gravity to a reservoir placed under the DynaSand filters. This reservoir has a capacity of 320 

m
3
and from here the water is pumped to the groundwater treatment plant. Direct filtration will consist of 

four lines with five DynaSand filters each making a total of 20 filters but with plans to increase to six 

lines (30 DynaSand filters ) later on (Norconsult, 2011). 

Carbon filtration 

Activated carbon is a water filtration method where chemicals are attracted or adsorbed to the filter 

media. This filter is effective in removing organic contaminants (trihalomethanes, pesticides and 

industrial solvents) from water but not that effective in removing microbes, sodium, nitrates and 

fluorides. Carbon filtration is also used to generally improve aesthetic aspects of the water such as taste 

and odour (LabWater, 2012). The carbon filtration, the water is temporarily stored in a raw water 

reservoir. 

2.2.2. Groundwater treatment plant 

Infiltration basins and abstraction wells 

The filtered water from the reservoirs in the surface treatment plant is then conveyed to the infiltration 

basins (see infiltration in section 2.1), percolated to the abstraction wells where it is pumped to a mixing 

tank prior to oxidation tanks. The proposed system will retain six of the existing wells and four new 

ones will be constructed (Norconsult, 2011). 

Oxidation 

Oxidation is the interaction of oxygen molecules and different substances they come in contact with. 

Water is oxidised by aeration (air and water contact) and the purpose of this aeration is to reduce carbon 

dioxide, to oxidise iron and manganese found in many well waters and to reduce ammonia and hydrogen 

sulphides (Water & Process Technologies, 2012). The infiltrated water from the wells is pumped to 

oxidation tanks where aeration takes place and sodium hydroxide is added to adjust the pH. The plant 

will have a total of five oxidation tanks, one tank used for mixing the water from the wells to obtain an 

even  water quality and  the rest of the tanks placed in two parallel lines through which the flow is 

divided.  



11 
` 

Rapid filtration 

The oxidised water flows by gravity to rapid sand filters and undergoes separation of the suspended 

particles from water. The water is filtered through a total of eight rapid sand filters in two parallel lines. 

UV disinfection 

Disinfection kills or inactivates pathogens in water supply to ensure that water is safe to drink. UV 

disinfection is the use of ultraviolet light to kill or hinder growth of pathogens in water. UV light 

penetrates the cell walls of an organism and disrupts the genetic material, hence making reproduction 

impossible and completely destroying the bacteria (Tech Brief, 2000). After the conventional filtration, 

the water will be disinfected by UV-lights and finally will undergo the last pH adjustment.   

The treatment plant also includes a chlorination system that will use Sodium hypochlorite (NaOCI) as 

the disinfectant if extra disinfection is needed. The treated water is stored in two reservoirs with a 

capacity of 5000 m
3
 before distribution. 

  



12 
` 

3. Microorganisms 

Microorganisms are a diverse group of unicellular or simple multi-cellular organisms. Water is habitat 

for many types of microorganisms, some of which are advantageous in their ability to degrade pollutants 

in water. However, other microorganisms cause illness and can even be life threatening. These disease 

causing microorganisms (pathogens) therefore become contaminants of drinking water.  

3.1 Waterborne pathogenic microorganisms 

Pathogenic microorganisms likely to cause waterborne diseases are mainly categorised as bacteria, 

viruses and protozoa and those most commonly concerning water supplies have been listed by the 

World Health Organisation –WHO (Appendix 1). This study takes into account eight types of pathogens 

falling in the three categories below (Table 1) because they are considered most likely to cause 

waterborne disease outbreaks in Sweden.  These pathogen categories are also a risk because of their 

persistence in colder waters and their resistance to chlorination (Svensk Vatten Utveckling, 2007).  

Table 1. Waterborne pathogens and their significance a in water supply (WHO, 2008) 

Pathogen 
Persistence in 

water supplies 

Relative 

infectivity 

Resistance 

to chlorine 

Important 

animal source 

Bacteria Campylobacter Moderate Moderate Low Yes 

 Salmonella Moderate Low Low No 

 EHEC Moderate High Low Yes 

Viruses Adenovirus Long High Moderate No 

 Norovirus Long High Moderate No 

 Rotavirus Long High Moderate No 

Parasites Cryptosporidium Long High High Yes 

 Giardia Moderate High High Yes 
 

3.1.1 Bacteria 

Bacteria are unicellular organisms that are present in every environment. They have widely varying 

properties and are capable of producing and consuming a variety of organic and inorganic matters. 

Almost every bacterium except some aquatic bacteria is sensitive to chlorination (Lundberg 

Abrahamsson, et al., 2009).   

Campylobacter 

Campylobacter can be found in both humans and animals, mostly in birds and can cause zoonotic 

infection i.e. it is able to transmit between human and animals (SMI, 2010). They survive for a few 

hours in environments at temperatures greater than 30 
o
C but can last several days at temperatures lower 

than 4 
o
C. Campylobacter has a low critical dose (800 – 100 000 ingested organisms) which means that 

at low level it can cause infection and possible disease. Infection of humans is usually characterised by 

diarrhoea, fever and abdominal cramps. Campylobacter infection may lead to severe but rare sequelae 

including arthritis (Hörman, 2005). Campylobacter is the most diagnosed microorganism in Sweden 

responsible for waterborne outbreaks.  Since 1980, 20 waterborne outbreaks of campylobacter have 

been reported with at least 2 000 infected individuals reported within the four major incidents. Due to 



13 
` 

lack of direct correlation between water-quality and conventional indicator organisms, the chance of 

finding the source of an outbreak is difficult (SMI, 2010). 

Salmonella 

Salmonella is widely found in nature and is present in most warm-blooded animal populations. 

Salmonella causes intestinal infection and one strain particular to humans causes typhoid fever. is 

Salmonella bacteria which also causes Salmonellosis, has over 2500 known serotypes through 

consumption of contaminated food and water. Its symptoms include fever, abdominal pain, diarrhoea, 

nausea and sometimes vomiting. It is especially dangerous to children and elderly causing dehydration 

and bloodstream infections (WHO, 2011). It has been noted that surface runoff contributes to 

Salmonella load in surface waters. This microorganism can reach the aquatic environment via the faeces 

of infected humans or animals through sewage discharge or runoff from agricultural areas (Levantesi, et 

al., 2011). 

Enterohaemorrhagic Escherischia coli (EHEC) 

Escherichia coli (E.coli) is commonly found in the intestinal tract of humans and warm-blooded 

animals. While most E.coli species are harmless, some species cause gastrointestinal disease. E.coli is 

categorized into seven groups including Enterohaemorrhagic Escherichia coli (EHEC) related to the 

0157:H7 serotype. This strain of E.coli has been linked to outbreaks of waterborne diseases. E.coli is 

transmitted mainly by faecal-oral route either through contaminated water and food or direct contact 

(AWWA, 2006). Symptoms of diseases caused by EHEC include abdominal cramps and diarrhoea that 

may proceed to bloody diarrhoea (haemorrhagic colitis) especially in children. Incubation period ranges 

from 3 – 4 days and infection may last for 10 days (WHO, 2011). 

3.1.2 Viruses 

In contrast to other cells, viruses are not able to metabolize on their own but instead require other 

organisms as host for their survival and reproduction. Viruses are characterised by their stability and 

their ability to infect all cells including microorganisms. Due to their small size (smallest known virus 

has a diameter of 10 nm), an accurate estimation of virus concentrations in water courses and their risk 

level of infection are difficult to obtain.  (Madigan & Martinko, 2006).More than 100 different types of 

enteric viruses have been identified in human faeces (Appendix1) and the ones common to Sweden has 

been considered for this study (Table 2).  It has been observed that the number of viruses excreted by 

humans may be more than one million per gram of faeces and a concentration of 500 000 infectious 

virus particles per litre of raw sewage has been detected in some parts of the world (Joseph, et al., 1978). 

At the water treatment facilities, a chain of treatment processes in combination will reduce the number 

of viruses either through physical removal barriers or barriers that provide inactivation and destruction. 

The conventional removal treatment process includes coagulation, flocculation, sedimentation and 

filtration. Destruction processes include inactivation in the presence of high pH, chemical oxidation by 

disinfectants and photo-oxidation by use of specific dyes combined with lights (Joseph, et al., 1978). 

Due to the lack of knowledge about viruses as live microorganisms and standardized analysis methods, 

no requirements in terms of log10-reduction or inactivation are recommended for the treatment processes 

of wastewater (Lundberg Abrahamsson, et al., 2009). 



14 
` 

Norovirus 

Norovirus are small ribonucleic acid (RNA) viruses with a high degree of genomic plasticity and 

capability to adapt to new environments. About 20 genotypes of Norovirus have been recognised and 

because of their wide inherent genetic variability are divided into five genogroups. Genogroups I and II 

(GI and GII) are responsible for human infection. Symptoms of Norovirus infections are typified by 

violent vomiting, high fever, diarrhoea, and headache. The infective dose for man is very low at about 

10 – 100 virus particles, with an incubation period of 1-3 days, and remaining infective for 2-3 weeks 

(Hörman, 2005). Norovirus (GII) is the main reason for winter vomiting disease in Sweden which is 

infectious and is transmitted through person to person contact, contaminated water and contaminated 

food. (Lund & Lindqvist, 2004). 

Rotavirus 

Rotaviruses are non-enveloped, double-stranded RNA viruses belonging to the family Reoviridae. These 

viruses have been divided into six groups, three of which infect human (groups A, B and C) s. In 

general, rotaviruses cause gastroenteritis, including vomiting and diarrhoea. The severity of the 

gastroenteritis can range from mild to severe and in some cases is fatal. In young children, extra-

intestinal manifestations, such as respiratory symptoms and seizures can occur. The incubation period is 

about 4–7 days and the illness generally lasts between 5 and 8 days. Theoretically, a single infectious 

virus particle is capable of causing infection; although more than one infectious virus particle is 

generally required (median infectious dose for rotavirus is 5.6) (Health Canada, 2010). 

Adenovirus 

Adenoviruses are members of the Adenoviridae family containing double-stranded DNA. At present, 

there are 51 serotypes of adenoviruses; about 30 % of these are pathogenic in humans, most causing 

upper respiratory tract infections. Serotypes 40 and 41 are the cause of the majority of adenovirus-

related gastroenteritis. Symptoms of adenovirus gastroenteritis include diarrhoea and vomiting. 

Adenoviruses are a common cause of acute viral gastroenteritis in children. Infections are generally 

confined to children less than 5 years of age and are rare in adults. The incubation period lasts 3 –10 

days, and illness may last a week. The viral load in faeces of infected individuals is approximately 108 

particles/g of faecal matter. This aids in transmission via the faecal–oral route, either through direct 

contact with contaminated objects or through recreational water and, potentially, drinking water (Health 

Canada, 2010). 

Table 2. Virus types common in Sweden and the infection caused 

Virus group Number of types Disease or sign caused 

Rotavirus  

(Reovirus family) 
3 

Epidemic vomiting and diarrhoea, 

mainly to children 

Adenovirus > 30 Respiratory disease, eye infections 

Norovirus 
20 

Violent vomiting, high fever, 

diarrhoea, and headache 
 



15 
` 

3.1.3 Protozoa 

Protozoa are a group of unicellular eukaryotic microorganisms without cell walls. They are colourless, 

motile and are larger than other comparable microorganisms like Prokaryotes that do not have a 

membrane-bound nucleus but their genetic information is in circular loop called a plasmid.  Protozoa are 

found in all types of aquatic environment and can grow in soil and in aerial habitats such as on the 

surface of trees. Most of the protozoa are parasitic and can be found in both humans and animals. 

Further, some species are zoonotic i.e can be transmitted from humans to animals (Madigan & 

Martinko, 2006). Protozoa thrive in cold water while indicator organisms like E.coli do not and 

therefore are difficult to detect by indicator organisms. Protozoa are resistant to disinfectants especially 

chlorination within the dosage that is usually recommended for drinking water treatment plants - DWTP 

(SVU, SMI, Livsmedelsverket, u.d.). 

In order to obtain safe drinking water, it is recommended that all water utilities suspect that all surface 

waters might contain the protozoan pathogens Cryptosporidium and Giardia and use this as a basis for 

planning and designing the treatment barriers (AWWA, 2006).  

Cryptosporidium 

It is estimated that Cryptosporidium has 20 species of which C. parvum and C. hominisare the most 

noted human enteropathogens. Cryptosporidium parvum is divided into genotypes 1 and 2 affecting 

humans and cattle respectively. Symptoms of cryptosporidiosis include diarrhoea, loose or watery 

stools, stomach cramps and a slight fever. Some infected persons show no symptoms. The incubation 

period is between 2 – 10 days and infection lasts two weeks (Hörman, 2005). Testing for 

Cryptosporidium is done by detecting oocysts. The oocysts are inactivated by freezing at -15 
o
C for 8 

hours. Increasing water temperature to 64.2 
o
C for two minutes or longer makes Cryptosporidium non-

infectious. Oocysts also become sensitive to drying after 4 hours when they are exposed to temperatures 

from 18 - 28
o
C (AWWA, 2006). Cryptosporidium enters water mainly through surface runoff from 

agricultural and pasture lands but also through sewage discharge.  

Giardia 

Giardia comprises six species that can infect a variety of hosts. Giardia duodenalis is infectious to 

humans but can infect other hosts too. Clinical infection is referred to as giardiasis and symptoms vary 

from severe diarrhoea to malabsorption (foul-smelling diarrhoea, abdominal pain, bloating, and nausea). 

Giardiasis may become chronic and illness may last for months. The incubation period ranges from 1 - 

14 days and illness usually lasts 1 – 3 days (Hörman, 2005). 

3.2 Faecal indicator organisms 

Methodological and interpretation limitations are still a concern for pathogen detection and monitoring 

in water. These limitations include the need for specialised laboratory equipment and highly trained 

personnel, the high cost of analysis, the need to determine which pathogens to test for and the number of 

pathogens present that may vary over time and space. Because of these limitations, routine monitoring 

of pathogens is not practical. However there are some organisms that can be routinely monitored and 

can be used as indicators for faecal contamination and the potential presence of enteric viruses. The 

indicators commonly used include E.coli, enterococci, Clostridium perfringens spores, and viruses of 

bacteria (bacteriophages). Total coliforms can also be used to provide a general overview of the water 

quality even if it is not used to indicate faecal pollution (Health Canada, 2010). 



16 
` 

The use of faecal indicators helps in the estimation of the microbiological quality of drinking water by 

reducing the complexity and cost of a direct analysis of pathogens (Åström, 2011). The World Health 

Organisation however recommends that specific criteria should be followed when determining and using 

faecal indicator organisms. The criteria for indicator selection can include;  

 General existence of the organisms in the faeces of humans and animals,  

 Ability to multiply in natural waters, 

 Same persistence properties in water as the faecal pathogens,  

 Presence in higher numbers compared to the pathogens and responding similarly  to treatment 

barriers as pathogens (WHO, 2008) 

The relationships between indicator organisms and the presence of pathogens in surface water sources 

have been investigated by several studies worldwide. Escherichia coli (E.coli) and Clostridium 

perfringens have been linked to the presence of pathogens caused by faecal contamination. 

Bacteriophages have also been related with the presence of enteric viruses. There are however some 

studies that have shown no correlation between indicators and pathogens. The most suitable indicator 

therefore depends on the surface water source and the site-specific faecal pollution inputs (Health 

Canada, 2010).   

E.coli and total coliforms have been used to verify the quality of drinking water. The presence or 

absence of E.coli does not strictly indicate presence or absence of pathogens. However, if the quality is 

monitored from the source to tap and each barrier in the drinking water system has been controlled to 

ensure proper operation, the presence or absence of E.coli and total coliform can be used as verification 

for whether the water is adequately treated (acceptable microbiological quality) (Health Canada, 2010) 

and (Livsmedelsverket, 2001).  

3.3 Water quality and health target 

Health targets are benchmarks to assist water suppliers to set up certain interventions in regard to source 

protection and treatment processes in order to provide safe drinking water. An accurate health target is 

dependent on local conditions including economic, environmental, and social/ cultural conditions, and 

financial, technical and institutional resources. Health-based targets for microbial pathogens reflect both 

control challenges, health hazards and other relevant data associated to a group of pathogens (WHO, 

2008). 

These targets are developed with regard to the microbial contaminant levels in water which would pose 

“acceptably low” risks of water borne infections to humans (Signor & Ashbolt, 2009). The World 

Health Organisation reference level of risk in relation to waterborne disease outbreak (the maximum 

frequency of infection, diarrheal disease or cancer incidence in terms of DALYs) is 1×10-6 
or one micro 

DALY (WHO, 2008). The commonly adopted benchmark is one used by the USEPA, which requires 

that the probability of an individual becoming infected by any type of reference waterborne pathogen 

following independent drinking water exposures over a year should not exceed        .” (Signor & 

Ashbolt, 2009). 

In Sweden, drinking water quality is controlled by the National Food Administration 

(Livsmedelsverket). Drinking water quality is deemed either suitable or not when samples taken at 



17 
` 

discharge point from the DWTP (effluent drinking water) and at the consumers tap are compared to the 

guideline value. The guideline values that separate the suitable drinking water from unsuitable are listed 

in Appendix 1. Swedish water utilities use USEPA guideline values to evaluate the efficiency of 

DWTPs and estimate the infection rate among population due to unsuitable drinking water. 

  



18 
` 

4. Material and Methods 

The aim of the study was to investigate the treatment processes being recommended for the proposed 

treatment plant. This required evaluating the efficiency of the treatment processes and the tool used in 

this study is a Quantitative Microbiological Risk Assessment (QMRA) tool. The study also used 

mathematical formulations to estimate pathogen concentrations in the raw water and the annual 

infection risks today as well as in future.  

The assessment of the new treatment system using QMRA was conducted using scenarios to determine 

the probability of infection to water consumers. Scenario zero, where infection risk is analysed using 

standard pathogens concentrations in the raw water estimated in the Swedish QMRA manual was 

studied together with three other scenarios described in sections 4.4, 4.5 and 4.6. In this study, both the 

current treatment plant and the new treatment system (scenario1) were considered. 

4.1 QMRA methodology 

QMRA is a methodology to quantify the risk of infection to drinking water consumers due to 

waterborne pathogen concentration in the source water. The Swedish Water & Wastewater Association 

has developed a QMRA tool which was used in this study. The methodology is based on four steps: 

hazard identification, dose-response formulation (effect assessment), exposure assessment and risk 

characterization (Figure 7) (Medema & Ashbolt, 2006).  

Performing a QMR investigates if the water supply system meets the health based target. In this study 

the USEPA guideline value is used with an  annual infection probability  not exceeding         which 

is one person infected out of 10 000 people exposed. This type of analysis gives a water supplier the 

opportunity to investigate if the water supply system (raw water quality and efficiency of the treatment 

barriers) under various circumstances and estimates the potential infection risks. Elimination of risks can 

be done by implementing risk reducing measures such as optimization of treatment processes and 

implement additional controls such as on-line monitoring systems (Medema & Ashbolt, 2006).  

 



19 
` 

 

Figure 7. Framework for the Microbial Risk Assessment in four steps (Medema & Ashbolt, 2006) 

4.1.1 QMRA input data 

The proposed treatment system (plant) has several microbial barriers (processes), suggested as presented 

in Figure 5, and four of the processes were focused on in this study. These are direct filtration 

(Dynasand), infiltration basins, rapid sand filters, and UV-light disinfection. The micro-strainers and 

carbon filters were not included in the QMRA model since they are not considered as microbial barriers 

and have negligible effect in removing pathogens. The chemical disinfection (chlorination) process is 

not included in the main QMRA model because UV-light is assumed to be effectively in inactivating 

pathogens. However chlorination is studied in one scenario but will only be used in case of emergencies 

e.g. when a pathogen outbreak occurs among populations in upstream wastewater systems and the 

wastewater overflows into in the source. This is then compared in to situation without chlorination.  

Removal efficiency 

In the QMRA model, the removal efficiency (log10-reduction) for each treatment process has to be 

defined and is formulated with triangular distribution (Appendix 1). In Table 3, the estimated log-



20 
` 

removal is presented for each barrier. The removal efficiency of the Dösebacka infiltration basins was 

calculated based on literature for all pathogens. The log10-reduction was set similar as for slow sand 

filters but was combined with literature data on removal efficiency addition per depth of the unsaturated 

zone. The additional removal efficiency due to the unsaturated zone was estimated as 0.5 log10 units per 

metre 
1
(Appendix 1).   

The removal efficiency of 20 Dynasand filters was used in the model although there is a proposal to 

increase the number to 30 according to Norconsult. The reason for using 20 instead of 30 is that if the 

system passes the USEPA guideline value (Annual infection risk < 10
-4

) with the lower number of 

filters, it can manage an even better result with an increased number of filters.  

Table 3. Removal efficiency (log10-reduction) for the treatment processes studied and other input data in the MRA 

model 

Treatment 

barrier 

Number of 

parallel 

lines 

Log10-reduction 

Bacteria Virus Protozoa 

Direct 

filtration 
a
 20 

Triangular 

(1,2.1,3.4) 

Triangular 

(1.2,3.0,5.3) 

Triangular 

(1.4,3.2,5.5) 

 

Infiltration 

basins 9 

Triangular 

(1.2,4.6,6.7) 

 

Triangular 

(0.6,4,6) 

 

Triangular 
C 

(0.3,5.7,8.4)
 

Triangular 
G
(1.2,5,8) 

 

Rapid sand 

filter 
a
 

8 
Triangular 

(0.1,0.6,1.5) 

Triangular 

(0.1,0.8,3.8) 

Triangular 
C
(0,2,3.1) 

Triangular 
G
(0,1.7,6.5) 

UV-light 

 
4 UV-lights Fluence dose=(40mJ/cm

2
)

b
 

a
 (Smeets, et al., 2006). 

b
 (Stanfield, et al., u.d.). 

C 
Cryptosporidium 

G
Giardia 

Calculated (estimated) pathogen concentrations 

In order to estimate the annual infection risk of pathogens passing through the treatment barriers, the 

pathogen concentrations in the raw water source need to be known. Pathogen concentrations based on 

measurements and calculated (estimated) values were applied. Moreover, standard pathogen 

concentrations from the Swedish QMRA manual were used in scenario zero which contains an 

estimation of annual infection risks for the current and new treatment plant (scenario 1) respectively.  

Data of measured protozoa concentrations in Göta älv was available (Appendix 2) but measured values 

for bacteria and viruses were not available. The concentrations of bacteria and viruses were therefore 

calculated using equation (2) below (Åström, et al., 2011). 

    
  

  
                        (2)        

   

                                       

                                                                  ) 

                                                           
1
 The value was estimated from discussions with Madelane Forss from Norconsults.  



21 
` 

                                                                      

                                                       

                                                        

 

The contents of studied bacteria, virus and E.coli in fresh faecal material are presented in Table 4. 

Table 4. Content of pathogens and indicator organism E.coli in faecal material of infected persons 

Pathogen Range (Median) Reference 

Campylobacter 10
6
-10

9 
(55.10

6
) (Guillermo, et al., 1996) 

EHEC 10
7
- 10

8 
(55.10

6
) (Klein, et al., 2008) 

Salmonella 10
5
-10

7 
(10

6
) (Schothorst & Beckers, 1978) 

Norovirus 10
6
-10

8 
(10

7
) (Pang, et al., 2004) 

Adenovirus 10
5
-10

11 
(10

8
) (Okoh, et al., 2010) 

Rotavirus 10
5
-10

11 
(10

8
) (Okoh, et al., 2010) 

Cryptosporidium 10
6
-10

7 
(55.10

5
) (Gerba, 2000) 

Giardia 10
3
-10

8 
(55.10

4
) (Smittskyddsinstitutet, 2011) 

E.coli 10
6
-10

8 
(10

7
) (Levy, et al., 1988) 

4.2 Estimation of annual infection risk 

According to results from the MICRORISK project, the annual probability of infections is assumed 

following a binomial process which refers to a series of trials with one of two possible outcomes 

(infection or not infection) (Petterson, et al., 2006). This daily probability of infection for an exposed 

individual is expressed as      and
 
then the daily probability of not being infected is        . The 

probability of not being infected over a period of time can therefore be expressed as         
n 

where 

  denotes the number of days, and for the annual probability of not being infected,           . The 

annual probability of infection risk therefore is determined using equation (3) below. The annual 

infection risk for one or more events occurring for a known duration (considering an expansion of the 

binomial model) over a year can be written as in equation (4). Equation (4) has been adjusted to suit the 

circumstances that have been considered for each scenario. 

                  
                                                                                                                                   (3) 

                        
                  

    

                                                                      (4) 

                                                                                 

                                           

                                                                      

                                    



22 
` 

The annual risk equation is adjusted to suit the scenarios to which it is applied. 

4.3 Current DWTP 

The risk of infection for the treatment plant currently used in Kungälv was calculated using QMRA 

model. The pathogen concentrations considered for the current DWT plant were measured and estimated 

concentrations according to equation (2) and using standard concentrations from the Swedish QMRA 

manual (Table 6) (Lundberg Abrahamsson, et al., 2009).  

Scenario Zero 

Annual infection risks for consumers receiving drinking water from the current and new DWTPs are 

studied using standard concentrations given in the Swedish QMRA manual.  

4.4 Scenario1 (Risks of infection - proposed treatment system) 

Scenario 1 studies the annual infection risks if the new treatment plant were in use today. The input data 

QMRA model is based on measured pathogen (protozoa) concentrations during the wet and dry periods 

(2005 – 2011) (Bergstedt, 2012) and calculated concentrations for bacteria and virus when there is no 

waterborne epidemic among the population connected to upstream wastewater system. Furthermore the 

proposed DWT plant was tested with the standard concentration of pathogens (scenario zero) from the 

Swedish QMRA manual (Table 6) (Lundberg Abrahamsson, et al., 2009). Annual infection rates for 

Scenario 1 have been studied as four cases (1, 2, 3, and 4), when some of the treatment processes are not 

functioning properly, using both the measured/estimated and standard concentrations of pathogen 

respectively. 

4.4.1 Pathogen concentration in Göta älv 

The measured concentrations of the protozoa are shown in Appendix 2. However the concentrations for 

viruses and bacteria have been calculated.  

Calculated concentrations 

Using equation (2), values of an indicator organism have to be used to calculate the pathogen content in 

the raw water source. E.coli has been selected as a preferred indicator organism in this study to estimate 

the pathogen concentrations. E.coli is the most commonly used indicator for water.  

The median concentrations of pathogens in faecal material (  ) derived from Table 4 have been used in 

equation (2). The median concentration of E.coli measured in Göta älv (  ) for four years period has 

been used (Table 5).  The risk level ( ) was assumed to be equal to 0.2 %  that is to say that 0.2% of the 

population connected to the upstream wastewater system is infected, providing the normal pathogen 

concentration in the source water (Ander & Forss, 2011). The estimated bacteria and virus 

concentrations are presented in Appendix 2 and the average calculated pathogen concentrations in Göta 

älv is shown in Table 6. 

 

 



23 
` 

Table 5. Measured median concentration of E.coli in Göta älv 

Year 
Median Concentration 

(CFU/100ml) 
Reference 

2002 90 (Kärrman, et al., 2004) 

2003 86 (GÖTA ÄLVS VATTENVÅRDSFÖRBUND, 2003) 

2004 98 (GÖTA ÄLVS VATTENVÅRDSFÖRBUND, 2004) 

2005 74 (GÖTA ÄLVS VATTENVÅRDFÖRBUND, 2005) 

 

Measured concentrations 

The measured concentrations of the protozoa Cryptosporidium and Giardia in Göta älv (Appendix 2) 

are 2005 to 2011 (Bergstedt, 2012). Moreover, standard concentrations (constant and mean values) used 

in scenario zero for the new treatment system are presented in Table 6. 

Table 6. Average concentration of pathogens in Göta älv 

Pathogen Average concentration per litre
1
 Standard concentrations

2
 

Campylobacter 9.57 

Calculated concentrations 

1 (constant value) 

EHEC 9.57 0.1(constant value) 

Salmonella 0.17 1 (constant value) 

Norovirus 1.74 1 (constant value) 

Adenovirus 17.4 1 (constant value) 

Rotavirus 17.4 1 (mean) 

Cryptosporidium 0.04 Measured concentrations  0.4 (mean) 

Giardia 0.04  0.5 (constant value) 

1
Both measured (protozoa) and estimated concentrations (bacteria and virus). 

2
From the Swedish QMRA manual (Lundberg 

Abrahamsson, et al., 2009) 

Due to variability of input data in the QMRA model, probability density function (PDF) was used to 

describe the variability of the water quality with time depending on catchment activity, seasonal climate 

variation and point source contamination such as CSOs. The Gamma distribution was chosen to describe 

the variability of pathogen concentrations. The parameters for the Gamma distribution for all pathogens 

are presented in Appendix 2. The standard concentration (during normal situations) of pathogens is 

expressed as a lognormal distribution as reported in the Swedish QMRA manual with parameters 

described in more detail in Appendix 2. 

Annual infection risk calculation 

The annual infection risk for all pathogens in scenario one when the new drinking water treatment plant 

was tested with the measured and calculated concentrations was collected from the QMRA model. In 

scenario zero, when the proposed DWT plant was tested with the standard concentrations (normal 

situations), the annual infection risk for all pathogens was collected directly from the QMRA model. 

4.4.2 Case 1- Annual infection risk without chlorination 

The proposed treatment processes (microbial barriers) except chlorination (not operating) were run in 

the QMRA model to calculate the risk of infection. It is proposed to use chlorination only when 



24 
` 

pathogens are detected in the raw water. This simulation in the model helps to asses if the proposed 

treatment plant will be efficient without chlorination.  

4.4.3 Case 2- Annual infection risk with chlorination 

Annual infection risk when all barriers, including chlorination are operating was determined. The 

purpose is to compare the pathogen removal efficiency when two disinfection barriers (UV-disinfection 

and chlorination) are operating  

According to the Swedish National Food Administration, the chlorine dosage should not exceed 1.0 

g/m
3
 at the plant and the level of chlorine in the drinking water provided into the distribution network 

should not be above 0.4 mg/l (Livsmedelsverkets författningssamling, 2011). 

The chemical disinfection used in the QMRA model is chlorine dioxide with a dosage of 0.4 mg/l. and 

has been simulated for two sub-cases. The first sub-case considers contact time based only on residence 

time within the distribution pipe network (no contact time in the reservoirs) and the second sub-case 

considers thee contact time based on residence time within both the reservoir and in the distribution pipe 

network (Appendix 2). The reason for this was to observe the difference on impact of chlorination in 

inactivating pathogens using two different contact times. Moreover this helps provide a recommendation 

of appropriate contact time for the chlorination to achieve optimal inactivation rate of pathogens. 

4.4.4 Case 3- Annual infection risk when one of the barriers is removed from the treatment plant 

Annual infection risk was determined when one barrier at a time is removed from the treatment train. 

The barriers studied in this case are direct filtration, infiltration basins, rapid filtration and UV 

disinfection. This case useful not only for determining the critical link within the treatment train but also 

to learn which part of the treatment system to control better to prevent critical breakdowns. 

The result is essential in planning and deciding treatment barriers combination, and to exclude a barrier 

from the treatment system in case of budget cut down in the project. The infiltration basins however are 

present at the current DWTP in Kungälv so this scenario may not be relevant for this barrier. However it 

was of interest to study the effect of removing from the treatment plant for a whole year even if that is 

not a realistic scenario. 

4.4.5 Case 4- Annual infection risk during maintenance of UV-disinfection units 

The new DWTP has four UV-units for inactivation of pathogens especially protozoa.  The UV-units use 

cleaning processes to provide the optimal removal efficiency they were designed for. Modelling the 

annual infection risk when two UV units are removed i.e. cleaning process activated helps to assess 

whether the consumers are safe from infection when maintenance is carried out. The maintenance 

process for both UV units was considered to take in average five days during a year. This maintenance 

operation was adjusted to suboptimal operation (two UV units operating below the design level) in the 

QMRA model and thus two UV disinfection units operate normally. 

The annual infection risk for viruses, bacteria and protozoa was calculated using equation (5) below 

adopted from the generic equation (4). The daily infection risks for normal condition and the sub-

optimal risk used in equation (5) are obtained from the QMRA results for case 1 and model simulations 

with two UV-units respectively.  



25 
` 

                             
       

                      
                                                  

                                               

                                                   

                                                           

                                                           

In scenario zero, considering the standard concentrations, the annual infection risk for all pathogens was 

estimated through equation (5). 

4.5. Scenario2 (Annual infection risk in 2060) 

The buildings of the proposed plant will be designed to last about 50 years and the mechanical parts to 

last about 30 years. It is therefore important to find out if this system will be capable of treating water 

efficiently in the future. Scenario two assesses the annual infection risk for the years between now and 

2060. This requires an estimation of the concentration of pathogens in Göta älv in 2060. 

4.5.1 Pathogen concentration estimation 

In order to estimate the concentration of pathogens in the year 2060, the standard pathogen 

concentrations given in the Swedish QMRA manual were used as a starting point. The pathogen 

concentrations were multiplied by an annual growth factor of 1.01 (1%) i.e. the standard concentrations 

from 2010 increased by 1% every year to 2060. The concentration of pathogen in the future will be 

affected by different factors such as precipitation, surface runoff and turbidity and can be estimated 

differently based on various factors (Pettersson 2012
2
). Here, the estimation of pathogen concentrations 

has been modelled to increase exponentially until year 2060. This is an appropriate assumption in the 

absence of other data for future concentration forecast. The exponential function equation (6) used for 

this assumption is expressed as; 

         
                                                                                                                                                           

                                                  

                                          

                                                          

In Table 7, average concentrations that have been estimated based on the exponential equation described 

above are presented. The calculation is presented in more detail in Appendix 3. 

 

 

 

                                                           
2
 Based on discussion with supervisor Thomas Pettersson during a consultation 



26 
` 

Table 7. Estimated average pathogen concentration 2060 

Pathogen Average concentration per litre 

Campylobacter 1.30 

EHEC 0.13 

Salmonella 1.30 

Norovirus 1.30 

Adenovirus 1.30 

Rotavirus 1.30 

Cryptosporidium 0.52 

Giardia 0.65 

 

The trend of estimated concentrations of pathogens from 2010 to 2060 is illustrated in Figure 8.   

 

Figure 8. Trend of pathogen concentrations in Göta älv from 2010 to 2060 

4.5.2. MRA model input data 

The input data of the removal efficiency for the treatment barriers and other data related to the barriers 

that are put into the QMRA model for the second scenario (year 2060) are same as for the first scenario 

(Table 3). Then the estimated Gamma parameters (shape and scale) for year 2060 (Appendix 3) were 

added into the model and simulation for each pathogen was performed. 

4.5.3. Estimation of annual infection risk 

The annual infection risks for the second scenario (year 2060) were calculated by the QMRA model. 

4.5.4. Pathogen removal efficiency by infiltration basins in 2060 

Studies of climate change state more extreme rain events are expected in future and this may result in 

rising water levels. This study considered that the infiltration basins along Göta älv together with the 

abstraction wells could be impacted by raising groundwater levels reducing the effectiveness of 

pathogens removal by the unsaturated zone and deterioration of water quality at the plant. 

0 

0,2 

0,4 

0,6 

0,8 

1 

1,2 

1,4 

1,6 

1,8 

campylobacter 

EHEC 

Salmonella 

Norovirus 

Adenovrus 

Rotavirus 

Cryptosporidium 

Giardia 



27 
` 

In case of a higher number of wet days, recharge of precipitation into the ground especially on the 

drumlin area which consists of permeable soil media might raise the groundwater table. This scenario is 

considered appropriate here to study the impact of the rising water table in reducing the unsaturated 

zone by 1 metre, in order to study the risk of infection when the removal efficiency of infiltration basins 

reduces.  On the other hand, when intensive rainy days are considered, surface runoff might stress the 

water quality in the basins if the basins are not protected at a certain level above the ground surface. The 

runoff could contain pathogens from animals’ faeces particularly from the forest area on the north side 

of the infiltration basins.  

In order to estimate the risk of infection due to the rise of the groundwater table by one metre, the log10-

reduction calculated in the QMRA model was changed. The new calculation considers an average 

unsaturated zone of 2.85 meters (1 meter decrease compared to the normal condition with 3.85 meters 

unsaturated zone) (Table 8).  

The annual infection risks in the case of raised groundwater table in the infiltration basins have been 

calculated by the QMRA model. 

 

Table 8. Log10- reduction in case of one-meter groundwater level rising in the infiltration basins 

Concentration 

 
Bacteria Virus 

Protozoa 

Cryptosporidium Giardia 

Minimum 1.2 0.6 0.3 1.2 

Likeliest 4 3.6 5.2 4.7 

maximum 6 5.4 7.9 7.4 

 

4.6. Scenario 3- Waterborne outbreak in upstream town 

This scenario considers that a waterborne disease outbreak of pathogens occurring upstream of Kungälv 

municipality. Possible sources are the cities of Lilla Edet and Trollhättan with combined sewer 

overflows that release wastewater to Göta älv. Such a waterborne disease outbreak on a population 

upstream of the Kungälv DWTP could be a source of contamination of the raw water, with a potential 

pathogen concentration that could infect the people in Kungälv. The concentrations of pathogens under 

this circumstance were estimated using equation (2). In equation (2) the risk level (p) or portion of 

people being infected in a specific moment has been estimated to be equal to      
 

  
 which a risk 

level of 40% reflects an infection of 40 % of a population upstream Kungälv during a specified period.
3 

The outbreak was assumed to last for a period of 2.5 months or 75 days. The number of days that a 

person is estimated to be infected was set to 7 days for all pathogens. Studying this scenario is useful 

                                                           
3
The largest outbreak between 1980 and 1999 in Sweden occurred in early 1988 and affected approximately 

11,000 people (with an attack rate of 41%) (Andersson & Bohan, 2001). In December 2011, the community of 

Östersund had a waterborne disease outbreak which affected 45% of the water consumers (FOI, 2012).  

 



28 
` 

when analysing how well the proposed treatment system can manage a possible outbreak contamination 

of raw water and what the consequences (infected individuals) would be if the treatment system fails. 

The consequence is compared to the USEPA guideline value. The estimated average concentrations for 

this scenario are presented in Table 9. 

Table 9. Average concentration of pathogens in the raw water source considering an outbreak upstream the DWTP of 

Kungälv 

Pathogen Average number of organisms per litre  

Campylobacter 179 

EHEC  179 

Salmonella 3.3 

Norovirus 33 

Adenovirus  325 

Rotavirus 325 

Cryptosporidium 18 

Giardia 1.8 

 

4.6.1 Estimation of annual infection risk 

The annual infection risk for bacteria, virus and protozoa is estimated by using equation (7) below: 

                                     
                            

                                (7) 

The normal daily infection risk is the risk during normal condition (no wastewater discharge into source 

water from upstream outbreak population) using the QMRA result from scenario1- case 1. The daily 

infection during an outbreak (                 was provided from the QMRA model using the estimated 

pathogens concentration due to an outbreak upstream of Kungälv. 

  



29 
` 

5. Results 

The risk assessment was performed for the current treatment system and the proposed treatment plant 

considering various scenarios. The results of the assessments are presented below for each scenario as 

annual infection risk, number of infected people and the log10-removal by treatment barriers. 

5.1. Annual infection (treatment plant currently used) 

In order to recommend the proposed treatment system, its annual infection risk has to be compared with 

the current treatment system. The treatment plant that is currently used in Kungälv was assessed 

regarding its effectiveness in removing pathogens in the raw water. The annual infection risks 

(estimated with measured/estimated concentrations) for all pathogens except Salmonella exceed the 

USEPA guideline value (      ) and the risk values are shown in Table 10. If the measured and 

calculated pathogen concentrations in scanerio1 (Table 6) were present in the raw water source, there 

would be a probability of more than 1 person out of 10 000 consumers suffering symptoms of diseases 

except for Salmonella. The daily infection risk for all pathogens is presented in Appendix 2. The number 

of people infected is also shown in the table. The annual infection risks when the standard 

concentrations (scenario zero) were used are presented in the same table which shows that for all 

pathogens except for Salmonella the annual infection risk exceed the USEPA guideline value. The 

number of infected persons by each pathogen except for Giardia in the scenario zero is remarkably 

lower in comparison to the ones with measured and estimated concentrations. 

Table 10. Annual risk of infection of the current DWTP in Kungälv  

Pathogen 
Annual infection risk -

Current DWTP
1
 

Number of people 

infected
1
 

Annual infection risk - 

Current DWTP
2
 

Number of people 

infected
2
 

Campylobacter 3.00E-01 7 500 1.10E-01 2750 

EHEC 4.50E-02 1 125 6.80E-04 17 

Salmonella 3.50E-06 0 2.00E-05 0 

Norovirus 2.60E-01 6 500 2.10E-01 5250 

Adenovirus 4.80E-01 12 000 1.80E-01 4500 

Rotavirus 4.90E-01 12 250 1.30E-01 3250 

Cryptosporidium 1.10E-02 275 2.80E-03 70 

Giardia 2.20E-04 6 3.70E-02 925 
1
When the estimated & measured concentrations were used. 

2
When the standard concentrations were used (Lundberg 

Abrahamsson, et al., 2009) 

The number of infected people due to the inefficient treatment by the current treatment plant with the 

measured/estimated and the standard concentrations at Dösebacka is shown in the bar chart below 

(Figure 9). The result shows that the Kungälv municipality needs to upgrade the existing plant, as it is 

planned to, in order to prevent possible infections among the population. 

 



30 
` 

 

Figure 9. The number of infected people from the current treatment plant 

5.2 Scenario1 (Infection risk under today’s conditions) 

In Scenario 1, the proposed treatment plant was checked for its efficiency in four cases (1, 2, 3 and 4) 

and the QMRA results for these four cases are shown below. 

5.2.1 Case 1- Annual infection risk without chlorination 

The simulated results from the QMRA model show that the annual infection risk for all pathogens does 

not exceed the USEPA guideline value (<10
-4

) except for Adenovirus (Table 11). The annual infection 

risk for Adenovirus would cause an infection of 40 individuals in the city of Kungälv. In scenario zero, 

when the standard concentrations were used, the annual infection risk for only Adenovirus is above the 

USEPA guideline value and causes infection for three persons (Table 11). The daily infection risk for 

each pathogen considering the measured/estimated and standard concentrations is shown in detail in 

Appendix 2. 

The pathogen removal in terms of log10-reduction by different barriers in the treatment system can be 

seen in bar chart below (Figure 10). It has been observed that the infiltration basin and UV disinfection 

are the most effective links regarding removal/inactivation of pathogens throughout the system.  Direct 

filtration is more effective for removal of viruses and protozoa than for removal of bacteria. UV 

disinfection has less effect in inactivating Adenovirus than the other viruses. The non-effective link in 

the system is rapid sand filtration regarding inactivation of all pathogens but especially for bacteria. 

Chemical disinfection (chlorination) has no effect on inactivating of Cryptosporidium and is most 

effect