Search

Most of the global population will live in urban areas in the 21st century. We study impacts of urbanization on future river pollution taking a multi-pollutant approach. We quantify combined point-source inputs of nutrients, microplastics, a chemical (triclosan) and a pathogen (Cryptosporidium) to 10,226 rivers in 2010, 2050 and 2100, and show how pollutants are related. Our scenarios consider socio-economic developments and varying rates of urbanization and wastewater treatment. Today, river pollution in Europe, South-East Asia and North America is severe. In the future, around 80% of the global population is projected to live in sub-basins with multi-pollutant problems in our high urbanization scenarios. In Africa, future river pollution is projected to be 11–18 times higher than in 2010, making it difficult to meet Sustainable Development Goals. Avoiding future pollution is technically possible with advanced wastewater treatment in many regions. In Africa, however, clean water availability is projected to remain challenging. Our multi-pollutant approach could support effective water pollution assessment in urban areas.

Microbial pollution is a major problem worldwide. High concentrations of Escherichia coli have been found in Kabul River in Pakistan. E. coli concentrations vary under different socio-economic conditions, such as population and livestock densities, urbanisation, sanitation and treatment of wastewater and manure, and climate-change aspects, such as floods and droughts. In this paper, we assess potential future E. coli loads and concentrations in the Kabul River using the Soil and Water Assessment Tool with scenarios that are based on the most recent Shared Socio-economic Pathways and Representative Concentration Pathways (SSPs and RCPs) developed for the Intergovernmental Panel on Climate Change (IPCC). Scenario_1 considers moderate population and livestock density growth, planned urbanisation and strongly improved wastewater and manure treatment (based on SSP1, "Sustainability"), and moderate climate change (RCP4.5, moderate greenhouse gas (GHG) emissions). Scenario_2 considers strong population and livestock density growth, moderate urbanisation, slightly improved wastewater treatment, no manure treatment (based on SSP3, "Regional rivalry") and strong climate change (RCP8.5, high GHG emissions). Simulated E. coli responses to Scenario_2 suggest a mid-century increase in loads by 111% and a late century increase of 201% compared to baseline loads. Similarly, simulated E. coli loads are reduced by 60% for the mid-century and 78% for the late century compared to the baseline loads. When additional treatment is simulated in Scenario_1, the loads are reduced even further by 94%, 92% and 99.3% compared to the baseline concentrations when additional tertiary treatment, manure treatment or both have been applied respectively. This study is one of the first to apply combined socio-economic development and climate change scenario analysis with an E. coli concentration model to better understand how these concentrations may change in the future. The scenario analysis shows that reducing E. coli concentrations in Pakistan's rivers is possible, but requires strongly improved waste water treatment and manure management measures.

Several model inter-comparison projects (MIPs) have been carried out recently by the climate, hydrological, agricultural and other modelling communities to quantify modelling uncertainties and improve modelling systems. Here we focus on MIP design for large-scale water quality models. Water quality MIPs can be useful to improve our understanding of pollution problems and facilitate the development of harmonized estimates of current and future water quality. This can provide new opportunities for assessing robustness in estimates of water quality hotspots and trends, improve understanding of processes, pollution sources, water quality model uncertainties, and to identify priorities for water quality data collection and monitoring. Water quality MIP design should harmonize relevant model input datasets, use consistent spatial/temporal domains and resolutions, and similar output variables to improve understanding of water quality modelling uncertainties and provide harmonized water quality data that suit the needs of decision makers and other users.

Rotavirus (RV) and diarrheagenic Escherichia coli are waterborne pathogens commonly causing diarrhea in children below five years old worldwide. Our study is a first step toward a loads-concentrations-risk modeling and scenario analysis framework. We analyzed current and future human RV and indicator E. Coli (EC) emissions from sanitation facilities to surface waters in Uganda using two process-based models. Emissions were estimated for the baseline year 2015 and for three scenarios in 2030 using population, excretion rates, sanitation types, and wastewater treatment. The first model is a downscaled GloWPa-Rota H1 version, producing emissions at a 1-km2 resolution. The second model is newly developed for Kampala and adds emissions from pit latrines and septic tanks excluded in the first model. The scenarios Business as Usual, Industrious, and Low Emissions reflect government prospects in sanitation coverage and wastewater treatment. For the first model, 6.14 ? 1014 RV particles d-1 and 1.31 ? 1012 EC colony-forming units (CFU) d-1 are emitted to surface waters in 2015. The RV emissions are expected to increase in 2030 by 75% for Business as Usual and 212% for Industrious and decrease by 58% in Low Emissions. Emissions from the second model are higher for Kampala than in the first model, at 3.74 ? 1014 vs. 5.95 ? 1013 RV particles d-1 and 8.18 ? 1011 vs. 1.75 ? 1011 EC CFU d-1 in 2015, most of which come from the onsite-not-contained category. Simulated emissions for Kampala show the importance of including onsite sanitation in our modeling. Our study is replicable in other locations and helps identify key emission sources, their hotspots, and the importance of wastewater treatment. The scenarios can guide future sanitation safety planning.

Microbial surface water quality is important, as it is related to health risk when the population is exposed through drinking, recreation or consumption of irrigated vegetables. The microbial surface water quality is expected to change with socio-economic development and climate change. This study explores the combined impacts of future socio-economic and climate change scenarios on microbial water quality using a coupled hydrodynamic and water quality model (MIKE21FM-ECOLab). The model was applied to simulate the baseline (2014-2015) and future (2040s and 2090s) faecal indicator bacteria (FIB: E. coli and enterococci) concentrations in the Betna river in Bangladesh. The scenarios comprise changes in socio-economic variables (e.g. population, urbanization, land use, sanitation and sewage treatment) and climate variables (temperature, precipitation and sea-level rise). Scenarios have been developed building on the most recent Shared Socio-economic Pathways: SSP1 and SSP3 and Representative Concentration Pathways: RCP4.5 and RCP8.5 in a matrix. An uncontrolled future results in a deterioration of the microbial water quality (+75% by the 2090s) due to socio-economic changes, such as higher population growth, and changes in rainfall patterns. However, microbial water quality improves under a sustainable scenario with improved sewage treatment (-98% by the 2090s). Contaminant loads were more influenced by changes in socio-economic factors than by climatic change. To our knowledge, this is the first study that combines climate change and socio-economic development scenarios to simulate the future microbial water quality of a river. This approach can also be used to assess future consequences for health risks.

User-friendly, evidence-based scientific tools to support sanitation decisions are still limited in the water, sanitation and hygiene (WASH) sector. This commentary provides lessons learned from the development of two sanitation decision support tools developed in collaboration with stakeholders in Uganda. We engaged with stakeholders in a variety of ways to effectively obtain their input in the development of the decision support tools. Key lessons learned included: tailoring tools to stakeholder decision-making needs; simplifying the tools as much as possible for ease of application and use; creating an enabling environment that allows active stakeholder participation; having a dedicated and responsive team to plan and execute stakeholder engagement activities; involving stakeholders early in the process; having funding sources that are flexible and long-term; and including resources for the acquisition of local data. This reflection provides benchmarks for future research and the development of tools that utilize scientific data and emphasizes the importance of engaging with stakeholders in the development process.

Lake Dianchi is severely polluted with nitrogen (N) and phosphorus (P). The effects of implementing environmental policies and technologies on future lake quality are not well understood. We analyse effects of environmental policies and technologies on future river export of nutrients into Lake Dianchi. We develop scenarios for 2050 and analyse these with the existing MARINA-Lakes model (Model to Assess River Inputs of Nutrient to lAkes). The scenarios differ in assumptions about future nutrient management in agriculture, sewage systems and mining. In the SSP3 (Shared Socio-economic Pathway 3) scenario, river export of nutrients to Lake Dianchi is projected to increase 1.4–4.4 times between 2012 and 2050. In the Current Policies scenario, rivers may export fewer nutrients than in SSP3, but this may not avoid eutrophication. Effects of improved nutrient management on river export of nutrients differ among nutrient forms, sub-basins and sources (e.g., urbanization in the north, agriculture in the middle and south). Pollution levels can be reduced below the 2012 level in an Optimistic scenario assuming advanced wastewater treatment, improved nutrient management in agriculture and no mining. However, even this may not completely prevent eutrophication. Preventing eutrophication requires even more efforts, for example, in implementing circular-oriented management options.

Sanitation planners make complex decisions in the delivery of sanitation services to achieve health outcomes. We present findings from a stakeholder engagement workshop held in Kampala, Uganda, to educate, interact with, and solicit feedback from participants on how the relevant scientific literature on pathogens can be made more accessible to practitioners to support decision-making. We targeted Water, Sanitation and Hygiene (WASH) practitioners involved in different levels of service delivery. Practitioners revealed that different sanitation planning tools are used to inform decision-making; however, most of these tools are not user-friendly or adapted to meet their needs. Most stakeholders (68%) expressed familiarity with pathogens, yet less than half (46%) understood that fecal coliforms were bacteria and used as indicators for fecal pollution. A number of stakeholders were unaware that fecal indicator bacteria do not behave and persist the same as helminths, protozoa, or viruses, making fecal indicator bacteria inadequate for assessing pathogen reductions for all pathogen groups. This suggests a need for awareness and capacity development around pathogens found in excreta. The findings underscore the importance to engage stakeholders in the development of support tools for sanitation planning and highlighted broader opportunities to bridge science with practice in the WASH sector.

Assessing global water quality issues requires a multi-pollutant modelling approach. We discuss scientific challenges and future directions for such modeling. Multi-pollutant river models need to integrate information on sources of pollutants such as plastic debris, nutrients, chemicals, pathogens, their effects and possible solutions. In this paper, we first explain what we consider multi-pollutant modelling. Second, we discuss scientific challenges in multi-pollutant modelling relating to consistent model inputs, modelling approaches and model evaluation. Next, we illustrate the potential of global multi-pollutant modelling for hotspot analyses. We show hotspots of river pollution with microplastics, nutrients, triclosan and Cryptosporidium in many sub-basins of Europe, North America and South Asia. Finally, we reflect on future directions for multi-pollutant modelling, and for linking model results to policy-making.

Highlights • Indicator organisms do not tell the whole story for the safety of water and sanitation systems. • Considering only fecal indicator groups may provide a falsely reduced sense of risk. • Consideration of pathogens matters for meeting SDG6., Water and wastewater utilities, water and sanitation hygiene (WASH) practitioners, and regulating bodies, particularly in developing nations, rely heavily on indicator microorganisms, as opposed to pathogens, for much of their regulatory decisions. This commentary illustrates the importance of considering pathogens and not relying only on indicator organisms when making decisions regarding water and sanitation, especially with respect to meeting the current targets of the Sustainable Development Goal (SDG) 6. We use quantitative microbial risk assessment (QMRA) to present three common scenarios that WASH and public health practitioners encounter to illustrate our point. These include 1) chlorination of surface water for drinking, 2) land application of latrine waste as a fertilizer, and 3) recreation/domestic use of surface waters impacted by wastewater discharge. We show that the calculated probabilities of risk of infection are statistically significantly higher when using treatment/survival information for pathogens versus using indicator species data. Thus, demonstrating that relying solely on indicators for sanitation decision making is inadequate if we truly want to achieve the SDG6 targets of safely managed water and sanitation services., Graphical abstract Image, graphical abstract

Microbial water contamination is a risk for human health, as it causes waterborne diseases like diarrhea. E. coli is a faecal indicator microorganism. Climate variables, such as temperature and precipitation, influence E. coli concentrations in surface and drinking water resources. We measure and statistically analyse E. coli concentrations in drinking and surface water in the Kabul River Basin. E. coli concentrations are very high in the basin. Drinking and bathing water standards are violated. Water temperature, surface air temperature, discharge and precipitation were positively correlated with E. coli concentrations. Precipitation induced runoff transports of E. coli from agricultural lands to Kabul River and high temperature coincides with high precipitation and discharge. A linear regression model was developed to assess the net effect of the climate variables on E. coli concentrations. We found that climate variables accounted for more than half of the observed variation in E. coli concentrations in surface (R2 = 0.61) and drinking water (R2 = 0.55). This study indicates that increased precipitation together with higher surface air temperature, as expected in this region with climate change, were significantly correlated with increased E. coli concentrations in the future. Waterborne pathogens are expected to respond similarly to hydro-climatic changes, indicating that disease outbreaks could well become more frequent and severe.

The protozoan pathogen Cryptosporidium is an important cause of diarrhoeal disease, but in many contexts its burden remains uncertain. The Global Waterborne Pathogen model for Cryptosporidium (GloWPa-Crypto) predicts oocyst concentrations in surface water at 0.5 by 0.5° (longitude by latitude) resolution, allowing us to assess the burden specifically associated with the consumption of contaminated surface water at a large scale. In this study, data produced by the GloWPa-Crypto model were used in a quantitative microbial risk assessment (QMRA) for sub-Saharan Africa, one of the regions most severely affected by diarrhoeal disease. We first estimated the number of people consuming surface water in this region and assessed both direct consumption and consumption from a piped (treated) supply. The disease burden was expressed in disability adjusted life years (DALYs). We estimate an annual number of 4.3 × 10 7 (95% uncertainty interval [UI] 7.4 × 10 6–5.4 × 10 7) cases which represent 1.6 × 10 6 (95% UI 3.2 × 10 5–2.3 × 10 6) DALYs. Relative disease burden (DALYs per 100,000 persons) varies widely, ranging between 1.3 (95% UI 0.1–5.7) for Senegal and 1.0 × 10 3 (95% UI 4.2 × 10 2–1.4 × 10 3) for Eswatini. Countries that carry the highest relative disease burden are primarily located in south and south-east sub-Saharan Africa and are characterised by a relatively high HIV/AIDS prevalence. Direct surface water consumption accounts for the vast majority of cases, but the results also point towards the importance of stable drinking water treatment performance. This is, to our knowledge, the first study to utilise modelled data on pathogen concentrations in a large scale QMRA. It demonstrates the potential value of such data in epidemiological research, particularly regarding disease aetiology.

Diarrhoea caused by waterborne pathogens still has a large burden of disease. We introduce a modelling and scenario analysis framework that enables better understanding of sources of and possible future changes in the disease burden due to environmental change and management implementation. The state-of-the-art research that can contribute to the development of the framework at the large scale is analysed, together with research gaps and opportunities for future research. Priorities have been identified and these include implementation of Quantitative Microbial Risk Assessment and application of the models in scenario analyses. The credibility of the model outputs should be central in the analysis, for example by developing stochastic models. Implementation of the framework contributes towards achieving the Sustainable Development Goals.

Climate change, comprising of changes in precipitation patterns, higher temperatures and sea level rises, increases the likelihood of future flooding in the Betna River basin, Bangladesh. Hydrodynamic modelling was performed to simulate the present and future water level and discharge for different scenarios using bias-corrected, downscaled data from two general circulation models. The modelling results indicated that, compared to the baseline year (2014&ndash, 2015), the water level is expected to increase by 11&ndash, 16% by the 2040s and 14&ndash, 23% by the 2090s, and the monsoon daily maximum discharge is expected to increase by up to 13% by the 2040s and 21% by the 2090s. Sea level rise is mostly responsible for the increase in water level. The duration of water level exceedance of the established danger threshold and extreme discharge events can increase by up to half a month by the 2040s and above one month by the 2090s. The combined influence of the increased water level and discharge has the potential to cause major floods in the Betna River basin. The results of our study increase the knowledge base on climate change influence on water level and discharge at a local scale. This is valuable for water managers in flood-risk mitigation and water management.

Cryptosporidium is a leading cause of diarrhoea and infant mortality worldwide. A better understanding of the sources, fate and transport of Cryptosporidium via rivers is important for effective management of waterborne transmission, especially in the developing world. We present GloWPa-Crypto C1, the first global, spatially explicit model that computes Cryptosporidium concentrations in rivers, implemented on a 0.5 × 0.5° grid and monthly time step. To this end, we first modelled Cryptosporidium inputs to rivers from human faeces and animal manure. Next, we use modelled hydrology from a grid-based macroscale hydrological model (the Variable Infiltration Capacity model). Oocyst transport through the river network is modelled using a routing model, accounting for temperature- and solar radiation-dependent decay and sedimentation along the way. Monthly average oocyst concentrations are predicted to range from 10−6 to 102 oocysts L−1 in most places. Critical regions (‘hotspots’) with high concentrations include densely populated areas in India, China, Pakistan and Bangladesh, Nigeria, Algeria and South Africa, Mexico, Venezuela and some coastal areas of Brazil, several countries in Western and Eastern Europe (incl. The UK, Belgium and Macedonia), and the Middle East. Point sources (human faeces) appears to be a more dominant source of pollution than diffuse sources (mainly animal manure) in most world regions. Validation shows that GloWPa-Crypto medians are mostly within the range of observed concentrations. The model generally produces concentrations that are 1.5–2 log10 higher than the observations. This is likely predominantly due to the absence of recovery efficiency of the observations, which are therefore likely too low. Goodness of fit statistics are reasonable. Sensitivity analysis showed that the model is most sensitive to changes in input oocyst loads. GloWPa-Crypto C1 paves the way for many new opportunities at the global scale, including scenario analysis to investigate the impact of global change and management options on oocysts concentrations in rivers, and risk analysis to investigate human health risk.

Devastating floods adversely affect human life and infrastructure. Various regions of the Hindukush-Karakoram-Himalayas receive intense monsoon rainfall, which, together with snow and glacier melt, produce intense floods. The Kabul river basin originates from the Hindukush Mountains and is frequently hit by such floods. We analyses flood frequency and intensity in Kabul basin for a contemporary period (1981-2015) and two future periods (i.e., 2031-2050 and 2081-2100) using the RCP4.5 and RCP8.5 scenarios based on four bias-corrected downscaled climate models (INM-CM4, IPSL-CM5A, EC-EARTH, and MIROC5). Future floods are modelled with the SWAT hydrological model. The model results suggest an increasing trend due to an increasing precipitation and higher temperatures (based on all climate models except INM-CM4), which accelerates snow and glacier-melt. All of the scenario results show that the current flow with a 1 in 50 year return period is likely to occur more frequently (i.e., 1 in every 9-10 years and 2-3 years, respectively) during the near and far future periods. Such increases in intensity and frequency are likely to adversely affect downstream population and infrastructures. This, therefore, urges for appropriate early precautionary mitigation measures. This study can assist water managers and policy makers in their preparation to adequately plan for and manage flood protection. Its findings are also relevant for other basins in the Hindukush-Karakoram-Himalayas region.

To improve microbial water quality and to prevent waterborne disease outbreaks, knowledge on the fate and transport of contaminants and on the contributions from different faecal sources to the total contamination is essential. The fate and transport of faecal indicators E. coli and enterococci within the Betna River in Bangladesh were simulated using a coupled hydrodynamic and water quality model. The hydrodynamic model for the river was set up, calibrated and validated with water level and discharge in our earlier study. In this study, the hydrodynamic model was further validated using measured water temperature and salinity and coupled with the water quality module. Bacterial load data from various faecal sources were collected and used as input in the water quality model. The model output corresponded very well with the measured E. coli and enterococci concentrations in the river; the Root Mean Square Error and the Nash-Sutcliffe efficiency for Log10-transformed concentrations were found to be 0.23 (Log10 CFU/100 ml) and 0.84 for E. coli, and 0.19 (Log10 CFU/100 ml) and 0.86 for enterococci, respectively. Then, the sensitivity of the model was tested by removing one process or forcing at a time. These simulations revealed that the microbial decay, the upstream concentrations and the discharge of untreated wastewater were the primary factors controlling the concentrations in the river, while wind and the contribution from the diffuse sources (i.e. urban and agricultural runoff) were unlikely to have a major influence. Finally, the model was applied to investigate the influence of wastewater treatment on the bacteria concentrations. This revealed that wastewater treatment would result in a considerable improvement of the microbial water quality of the Betna River. This paper demonstrates the application of a comprehensive state-of-art model in a river in a data-poor tropical area. The model can potentially be applied to other watersheds and can help in formulating solutions to improve the microbial water quality.

The likelihood of leafy green vegetable (LGV) contamination and the associated pathogen growth and survival are strongly related to climatic conditions. Particularly temperature increase and precipitation pattern changes have a close relationship not only with the fate and transport of enteric bacteria, but also with their growth and survival. Using all relevant literature, this study reviews and synthesises major impacts of climate change (temperature increases and precipitation pattern changes) on contamination sources (manure, soil, surface water, sewage and wildlife) and pathways of foodborne pathogens (focussing on Escherichia coli O157 and Salmonella spp.) on pre-harvested LGVs. Whether climate change increases their prevalence depends not only on the resulting local balance of the positive and negative impacts but also on the selected regional climate change scenarios. However, the contamination risks are likely to increase. This review shows the need for quantitative modelling approaches with scenario analyses and additional laboratory experiments. This study gives an extensive overview of the impacts of climate change on the contamination of pre-harvested LGVs and shows that climate change should not be ignored in food safety management and research.

Environmental variables influence Faecal Indicator Bacteria (FIB) in surface water. Understanding that influence is important, because presence of FIB, which are an indication of faecal contamination, means that harmful pathogens could be present that could also be influenced by environmental variables. Although some recent studies have focused on this topic, most of this work has been conducted in developed countries. Similar studies in developing countries and in a (sub)tropical climate are lacking. In this study we assess the influence of environmental variables on fluctuations in FIB concentrations of the Betna River in southwest Bangladesh that floods almost every year. Monthly water samples from five locations along Betna River were tested for FIB (E. coli and enterococci) in 2014–2015. A linear regression model was developed to assess the effect of the environmental variables on FIB concentrations. The study revealed increased FIB concentrations during wet weather conditions. Precipitation and water temperature were positively correlated with FIB concentrations. Water temperature was positively correlated, because the warm May to September period coincides with frequent precipitation. Precipitation increases manure release from land to surface water. The regression model explains nearly half of the variability in FIB concentrations (R2 of 0.46 for E. coli and 0.48 for enterococci). This study indicates that increased precipitation combined with higher water temperature, as is expected in this region with climate change, likely increases FIB concentrations. Waterborne pathogens are expected to respond similarly to these environmental changes, indicating that disease outbreaks could well become more frequent and severe.

Angular-distance weighting (ADW) is a common approach for interpolation of an irregular network of meteorological observations to a regular grid. A widely used version of ADW employs the correlation decay distance (CDD) to (1) select stations that should contribute to each grid-point estimate and (2) define the distance component of the station weights. We show, for Europe, that the CDD of daily precipitation varies spatially, as well as by season and synoptic state, and is also anisotropic. However, ADW interpolation using CDDs that varies spatially by season or synoptic state yield only small improvements in interpolation skill, relative to the use of a fixed CDD across the entire domain. If CDDs are optimized through cross validation, a larger improvement in interpolation skill is achieved. Improvements are larger for the determination of the state of precipitation (wet/dry) than for the magnitude. These or other attempts to improve interpolation skill appear to be fundamentally limited by the available station network

In many world regions the availability of clean water is at risk. Pollution of rivers and coastal seas poses a threat to aquatic ecosystems and society. Here, we review representative examples of mathematical models that simulate pollutant flows from land to sea at global and continental scales. We argue that a multi-pollutant modelling approach would help to better understand and manage water quality issues. Pollutants often have common sources and multiple impacts. Most existing spatially explicit models, however, focus on one type of pollution only. A new generation of models is needed to explicitly address the combined exposure of surface waters to multiple pollutants. Such models could serve as a basis for integrated water quantity and water quality assessments.

Cryptosporidium is a pathogenic protozoan parasite and is a leading cause of diarrhoea worldwide. The concentration of Cryptosporidium in the surface water is a determinant for probability of exposure and the risk of disease. Surface water concentrations are expected to change with population growth, urbanisation and changes in sanitation. The objective of this paper is to assess the importance of future changes in population, urbanisation and sanitation on global human emissions of Cryptosporidium to surface water. The GloWPa-Crypto H1 (the Global Waterborne Pathogen model for Human Cryptosporidium emissions version 1) model is presented and run for 2010 and with scenarios for 2050. The new scenarios are based on the Shared Socio-economic Pathways (SSPs) developed for the climate community. The scenarios comprise assumptions on sanitation changes in line with the storylines and population and urbanisation changes from the SSPs. In SSP1 population growth is limited, urbanisation large and sanitation and waste water treatment strongly improve. SSP1* is the same as SSP1, but waste water treatment does not improve. SSP3 sees large population growth, moderate urbanisation and sanitation and waste water treatment fractions that are the same as in 2010. Total global Cryptosporidium emissions to surface water for 2010 are estimated to be 1.6 × 1017 oocysts per year, with hotspots in the most urbanised parts of the world. In 2050 emissions are expected to decrease by 24% or increase by 52% and 70% for SSP1, SSP3 and SSP1* respectively. The emissions increase in all scenarios for countries in the Middle East and Africa (MAF) region, while emissions in large parts in Europe decrease in scenarios SSP1 and SSP3. Improving sanitation by connecting the population to sewers, should be combined with waste water treatment, otherwise (SSP1*) emissions in 2050 are expected to be much larger than in a situation with strong population growth and slow development of safe water and improved sanitation (SSP3). The results show that population increase, urbanisation and changes in sanitation should be considered when water quality and resulting health risks are estimated by water managers or public health specialists.