Why over 350 million people in the Ganges catchment live near rivers too polluted for bathing or irrigation

By Dave Milledge and Josh Bunce.

Last year, we published an article about water quality in the Ganges. The key points were really important but we realised, as we started talking to people about the work, that the points got a little lost amidst some of the technical detail. So we have written this summary piece to help make the work we did more accessible. For those short on time, we have published an even shorter piece on the same topic in The Conversation: Ganges: sewers could be making water quality of India’s great river worse.

Indian rivers are an important resource for everyday life yet pollution puts those that use them in danger [photo: Ashesh Shah / CC BY-SA 4.0]

Key points

  • Urban populations contribute around 100 times more microbial pollution per head to the river than their rural counterparts.
  • For 79% of the population of the Ganges catchment their nearest river fails faecal coliform standards for irrigation waters (it’s even higher if you go for other limits like safe bathing).
  • Untreated sewage discharged from a sewer is worse for river water quality than having no sewers.

Background

For millions of people worldwide, sewage-polluted surface waters threaten water security, food security and human health. Unclean water poses significant risks of diarrhoea, opportunistic infections, and consequent malnutrition accounting for around 1.7 million deaths annually; of which more than 90% are in developing countries and almost half are children. These deaths are primarily due to ingestion of faecal pathogens from humans or animals. Yet the extent of surface water pollution and its causes are poorly constrained, particularly in places where this water is most important to everyday life. Given rapid widespread global urbanisation, the impact of urban versus rural populations on surface water pollution is particularly important but unknown.

India’s growing population and economy are driving rapid urbanisation (30% of people now live in urban areas) and are exerting increased pressure on surface and groundwater availability. In rural areas around 67% of the population defecate in the open, a practice that poses severe risk to both health and safety. While in urban areas around 80% of the population have access to a toilet, only around 30% are connected to a sewer and few sewers are connected to a sewage treatment plant. The impact of these sanitation problems on surface water quality has been documented for many years at individual sample locations or river reaches across India. The Ganges catchment with its estimated 500 million inhabitants is a high profile example. However, no one has previously tried to quantify the problem for the catchment as a whole and there has been limited indication of what is driving it. The former shows us the scale the problem, while the latter shows us ‘what to do where’. Urban areas are often the dominant source of sewage pollution in rivers but, is this simply because there are more people in the same space or because urban people contribute more pollution?

Our approach

We address this question using water quality data from across the Ganges catchment and show the pattern of sewage pollution in its major rivers. We compare instream concentrations of faecal coliforms (a common pollution indicator found in human and animal faeces) with upstream densities of the two major pollution sources (humans and livestock) at 100 sites across an area of 1 million km^2 . Faecal coliform (FC) concentrations in the water samples were estimated using a standardised technique and are reported as the most probable number (MPN). At each site, we took a simple average of 10 years of data (2002–2012) with most sites having at least monthly measurements. To estimate upstream population density we used census data from 2000 and to estimate the contribution from animals we used FAO livestock density data.

Main results

Extreme pollution levels have been widely reported in the Ganges catchment and, in that respect, our results made depressing but familiar reading. At the most polluted site, the average FC concentration was 2.5 million MPN per 100ml. That is the lower end of what you would expect to find in raw sewage! It is worth noting that this was an average calculated over a decade so while some days it will have be less polluted, others it will have been more.

Collating results from multiple rivers showed that previous reports of high FC concentrations in local areas, may be a result of extensive pollution across the catchment rather than isolated pockets of poor water quality. The result is that 70% of sites fail Indian Government desirable bathing limits with those that do pass being located almost exclusively in the sparsely populated Himalayan catchments. On the more populous plains, 88% of sites fail the desirable bathing water limits which supports our finding that locally high FC concentrations are generally associated with large population centres (Figure 1), most markedly for smaller rivers like the Varuna at Varanasi. This becomes particularly relevant when we consider the high amenity of waters close to urban areas, such as the pilgrimage sites of Haridwar and Rishikesh. However, FC concentrations are moderately reduced downstream of the Yamuna-Ganges confluence as tributaries with lower FC concentrations dilute the main river. Further downstream, even large cities like Patna have limited influence on pollution concentrations, probably because of the sheer volume of water passing through Patna every day as the Ganges drains its enormous catchment. That’s not to say the volume of sewage passing through the Ganges at Patna has reduced relative to that upstream (at Kanpur for example), in fact by volume there’s a lot more, but there’s also much more water to dilute it. Nor does it indicate that cities downstream are delivering less pollutant, the bigger the volume flowing through a place every day, the less difference a little more or less pollutant will make to the concentration. Adding a single teabag makes a big difference to a cup of water but a tiny difference to a swimming pool full.

Figure 1. Network graph of decadal mean FC concentrations (circle colour) and catchment area (circle size). Large red circles indicate high FC concentration and water discharge (thus high FC flux); smaller green circles indicate lower concentration and discharge (thus low FC flux). Sites with thick blue outlines pass Indian Government desirable standards of <500 MPN / 100 ml; those with thin blue outlines pass the upper limit of <2500 MPN / 100 ml. Rivers are labelled in blue; cities are labelled in black, with approximate populations, in millions, in brackets and grey boxes to show approximate extent. Inset shows a location map of the Ganges catchment. [from Milledge et al., 2018]

As expected, FC concentration increases when the density of people living upstream increases (see Figure 2). Upstream population density is a good predictor of instream FC concentrations across the Ganges catchment, explaining 73% of the observed variation in decadal mean FC concentrations. This is consistent with findings from catchments across the world and with previous findings in the Ganges Catchment. However, we were particularly interested in the shape of the relationship between population density and FC concentration. It is not linear, instead, FC concentration is not only greater at higher upstream population densities but also increases more rapidly. This can be interpreted as the change in FC contribution per person as upstream population density increases. It strongly suggests that urban populations contribute more pollution per person than rural populations and that how much more depends on their respective population densities. A person living in an area with 1000 people per km^2 contributes on average 100 times more pollution to the river than they would in an area with 100 people per km^2. While this is an average in the presence of considerable variability, the denser population in this case contribute at least as much pollution per person and up to 10,000 times more (Figure 2).

Figure 2: catchment scale analysis of faecal coliform concentration against upstream population density. Three different trend lines (solid quadratic, dotted cubic and dashed linear spline) all give very similar results. Solid grey lines show the expected relationship between FC concentration and upstream population density assuming equal per head contribution. [modified from Milledge et al., 2018]

Does more people mean more poo?

The increase in per head impact as upstream population density increases cannot be explained by changes in FC production (i.e. urban people are unlikely to produce sufficiently more poo or more FC’s). Instead it likely reflects an increase in the efficiency with which FCs are delivered to the river, perhaps due to changes in individual or corporate waste management decisions as population density increases. At low population densities, much of the population defecate in the open or in pit latrines where faeces are less likely to be washed into the river and FCs are more likely to die in situ. As population density increases and towns and cities grow, the distance to open fields increases and there is a need for an alternative strategy to manage faeces. Sewage systems vary in sophistication but generally involve the movement of excreta by water out of the population centre. The faeces have a much shorter residence time in the environment and FCs will be removed primarily by sewage treatment rather than die-off in the environment. In many Indian cities, the flux of sewage that is, and must be, removed from the population centre through a growing network of sewers and storm water drains is many times higher than the capacity of the sewage treatment facilities. In this case the predominant impact of the sewage network is to remove the sewage from the population centre and rapidly deliver it to the river untreated. Sewage removal is essential for the public health of the city, but without effective treatment it comes at the cost of accentuated river pollution with associated public health implications for the population downstream. Our work demonstrates the severe river pollution that results. The extent to which this can be addressed by following the same trajectory towards centralised ‘end-of-pipe’ sewage treatment has been called into question for practical and economic reasons. However, there is a growing range of innovative, water and energy efficient, on-site and decentralized alternatives as well as a growing recognition that this is a social as well as physical or technical issue.

It is important to emphasise that our results do not imply that open defecation is a safe approach to sewage management. Water is not the only vector for faecal-oral disease; transmission can also occur through food, insects, and direct contact. Thus, safely disposing of faeces involves more than simply ensuring that they do not enter the watercourse. There is, of course, good evidence to suggest that open defecation is extremely problematic for public health and safety.

How dirty is the Ganges?

We used the relationship between upstream population density and FC concentration at our sites to predict FC concentrations across the entire Ganges catchment. This model predicts that 33–48% of rivers, fail the Indian Government’s safe bathing standards, depending on the choice of standard (Figure 3). However, many of those rivers that pass are in sparsely populated areas. For 70–85% of the catchment’s population, their nearest river fails safe bathing standards; for 79% it should not be used for flood irrigation, irrigation of crops eaten raw or where children are involved in farming; and for 51% it should not be used for irrigation with sprinklers.

Figure 3. Spatial pattern of predicted coliform concentration. Dark blue areas have concentrations below 500 MPN/100ml, the Indian Government’s desirable limit for safe bathing; light blue areas have concentrations below 2500 MPN/100ml, the upper limit for safe bathing. The inset shows the fraction of the river network (blue) and population (red) for which the nearest river has an FC concentration less than the x-axis value. Letters signify: (a) USA limit for safe bathing; (b) Indian government desirable limit for safe bathing; (c) WHO recommended limit for flood irrigation, or for crops eaten raw, or where children are involved in farming; (d) Indian government upper limit for safe bathing; (e) WHO limit for sprinkler irrigation. [from Milledge et al., 2018]

The pattern of predicted FC concentration is strongly influenced by the specific pattern of the river network (Figure 3). Sparsely populated Himalayan catchments produce high discharges of clean water suppressing FC concentrations far downstream; without this discharge, plains-fed rivers like the Kali have high FC concentrations throughout. Interventions high up the river network have the highest potential for impacting FC concentration for a given FC flux reduction for two reasons. First, lower discharge on these rivers means that the same FC flux reduction will lead to a larger concentration reduction. This is the teacup vs swimming pool effect again: preventing the teabag being dunked (i.e. treating the sewage) will have far less effect on the swimming pool (a high discharge river) than it will on the cup (a small river). Second, rivers are directed networks (i.e. they accumulate) thus a reduction in FC flux at a given location will impact only reaches downstream of it. If you remove sewage high up the network every place that sewage would have passed through benefits. Decisions of what to do where are difficult and necessarily political, with many drivers but the findings of this study can help guide strategic investment in pollution reduction.

Our approach neglects many processes that should be important in the transport of coliforms from source to the point of measurement (e.g. weather dependent die-off rates, hydrological connectivity, hydraulics at the cross section and reach scale). However, it is encouraging that even our simple empirical model explains a large fraction of the variance in microbial pollution concentrations.

What about the livestock?

Faecal coliforms are found in both human and animal faeces so one key issue that we needed to address in this study was the extent to which the FC signal that we observed was due to human rather than animal sources. When we examined the whole Ganges catchment it was difficult to separate human population density and livestock density and both were good predictors of faecal coliform concentrations in the river. However, when we broke the catchment into smaller chunks, whilst the relationship between source and river quality was similar, the sources become de-correlated (e.g. in cities, where population density is high but livestock density low). When we examined the data using these smaller chunks we found that population density remained a good predictor but that livestock density was no longer as useful. This is really important because it suggests that the humans rather than the livestock are primarily responsible for the faecal coliforms in the rivers. The health risk from human sewage is much higher than that from animal faeces because humans are less likely to get sick from pathogens that are found in animal faeces compared to those found in human faeces.

What about coliform die-off?

Faecal coliforms are not a conservative tracer, they die off once they are outside the gut so a second key issue that we needed to address was the potential impact of this die-off on our results. We did this by adjusting the population and livestock densities using a distance-decay function then seeking decay parameters that maximised the performance of our statistical model. We found that including a distance-decay function did not improve our ability to predict FC concentrations; and that the reduction in performance related to a reduction in decay-adjusted population density primarily at sites with intermediate or dense populations. The first finding suggests that faecal coliform die-off is not exerting a strong influence on the findings while the second finding suggests that as a result even cities far upstream can have an impact on downstream faecal coliform concentration. It isn’t only your nearest city upstream that is responsible for the microbial pollution in fact almost everyone upstream contributes.

Conclusion

The rivers of the Ganges catchment are subject to widespread and, in places, severe microbial pollution. 52–67% of the sites we measured fall below the Indian Government’s upper and desirable limits for safe bathing. For 70–85% of the population, an estimated 350–425 million people, their nearest river falls below these same bathing standards. The entire population living upstream contribute to microbial river pollution and not just those living near by the river. However, urban populations contribute more pollution per person than rural populations: on average 100 times more! This is likely a result of the efficiency with which untreated sewage reaches the river as well as there simply being more people. The urgency, therefore, to invest in sewage collection and treatment infrastructure has never been greater, especially in the most density populated areas.

This article is a technical summary of the journal article: “Population density controls on microbial pollution across the Ganga catchment” which I co-wrote with Suresh Gurjar, Joshua Bunce, Vinod Tare, Rajiv Sinha and Patrice Carbonneau; published in Water Research in 2018 and that you can read for free at: https://www.sciencedirect.com/science/article/pii/S0043135417308709.