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Monitoring and Analysing the Impact of Industry on the Environment
Monitoring and Analysing the Impact of Industry on the Environment
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For millions of people worldwide, sewage-polluted surface waters threaten water security, food security and human health. Yet the extent of the problem and its causes are poorly understood. Given rapid widespread global urbanisation, the impact of urban versus rural populations is particularly important but unknown.
Exploiting previously unpublished archival data for the Ganga (Ganges) catchment, we find a strong non-linear relationship between upstream population density and microbial pollution, and predict that these river systems would fail faecal coliform (FC) standards for irrigation waters available to 79% of the catchment’s 500 million inhabitants. Overall, this work shows that microbial pollution is conditioned by the continental-scale network structure of rivers, compounded by the location of cities whose growing populations contribute circa 100 times more microbial pollutants per capita than their rural counterparts.
Rising demands on water resources raise concerns about the sustainable provision of clean water worldwide. Unclean water poses significant risks of diarrhoea, opportunistic infections, and consequent malnutrition accounting for approximately 1.7 million deaths annually, of which almost half are children.
While this is a global issue, India’s growing population and economy in particular are driving rapid urbanisation and exerting increased pressure on surface and groundwater availability.
The impact of sanitation problems on surface water quality (in rural areas approximately 67% of the population defecate in the open) has been documented for many years, but there has been no catchmentwide quantification of the problem. Urban areas often dominate the microbial pollution signal in rivers, but there is little consensus on the extent to which this reflects an increased impact per capita or simply a larger population and thus source. This difference is important, since if it can be attributed to per capita contribution this will define the extent to which urban or rural focused interventions will improve surface water quality.
To address this question, we use 10 years of archival water quality data to show the pattern of microbial pollution at 100 sites in the Ganga cathment’s major rivers, spanning an approximate surface area of 106 km2.
“the use of FCs for monitoring pollution is still regarded as a viable measure of drinking and irrigation water quality”
Faecal pathogens are difficult to measure; however, thermo-tolerant coliforms, which originate in faeces (i.e. faecal coliforms, FC), are easily detectable and are routinely monitored as indicator organisms. New host-specific tracing techniques allow more precise tracking of microbial pollution sources that can help to better assess risks to human health. However, such techniques are not used within routine monitoring in India and thus do not have the spatial coverage required for our analysis. Furthermore, the use of FCs for monitoring pollution is still regarded as a viable measure of drinking and irrigation water quality.
To estimate upstream population density we used the GPWv3 gridded synthesis of census data from 2000. To estimate livestock density we used the FAO global gridded livestock density data, weighted by estimates of FC production rates for each livestock type (cow and buffalo: 1011 MPN/# day; goats and sheep: 1.2 × 1010 MPN/# day; pigs: 1.1 × 1010 MPN/# day; poultry: 1.4 × 108 MPN/# day). Upstream area, upstream population density (UPD) and upstream livestock density (ULD) for each sample point were calculated using a D8 flow routing algorithm and the hydrologically corrected 90 m SRTM DEM. To examine the influence of coliform die-off in transit and thus relax the assumption that coliforms behave as conservative tracers we introduced an exponential decay in coliform concentration with distance from the source. We sampled the shape parameter that defines the rate of distance decay at 500 logarithmic intervals from 10-8 to 10-1 km−1 testing model performance in each case using ordinary least squares regression.
Our results suggest that high FC concentrations previously reported at the reach and sub-catchment scale do not reflect isolated pockets of poor water quality, but extensive pollution across the catchment. Decadal mean FC concentrations at sites across the Ganga catchment range from 3 × 100 to 2.5 × 106 MPN/100 ml. 70% of sites fail Indian Government desirable bathing limits, with those that pass located almost exclusively in the sparsely populated catchment headwaters. On the more populous plains, 70 of the 80 sites fail the desirable limits and 63 of the 80 sites fail the maximum permissible 2500 MPN/100 ml limit. Locally high FC concentrations are generally associated with large population centres (Fig. 1), most markedly for rivers with smaller catchment areas (e.g. the Varuna at Varanasi).
Since people and livestock are the primary sources of FCs, we expect FC concentration to increase with the upstream density of these sources. Fig. 2 suggests that the data fit this expectation.
The relative importance of human or livestock FC sources
Both UPD and ULD are good predictors of FC concentration based on catchment scale analysis. When calculated over large areas population and livestock density are highly correlated, but at small scales population and livestock density can become de-correlated (e.g. in cities, where population density is high but livestock density low). Our sub-catchment based analysis breaks the catchment into smaller non-nested segments, disrupting the correlation between UPD and ULD (Fig. 3c). This analysis shows a small reduction in the percentage of variance in FC concentration explained by UPD and a large reduction in that explained by ULD. In the sub-catchment based analysis UPD is a much better predictor of FC concentration than ULD.
This is consistent with simple accounting estimates of export coefficients calculated using population and livestock densities, with estimated FC production rates for the loading terms and observed FC concentration as the output. Assuming a human production rate of 2 × 109MPN/# day and livestock production rates detailed in the methods section, livestock-derived FC loads produced on any given day range from 2 × 1010MPN/km2 day (for ULD = 3 #/km2) to 1.5 × 1013 MPN/ km2 day (for ULD = 200 #/km2) while population derived FC loads range from 1.4 × 1010 MPN/km2 day (for UPD = 7 #/km2) to 2 × 1012 MPN/km2 day (for UPD = 1000 #/km2). Yet over this range of source densities FC concentrations increase from 2 × 100 to 1 × 105 MPN/100 ml on average. This results in export coefficients >100 times larger at high livestock and population densities than at low densities. It is difficult to conceive of a mechanism for such an increase in export coefficient for livestock-derived FCs as a function of source density.
The relative importance of local or non-local FC sources
UPD is a good predictor of instream FC concentrations across the Ganga catchment, explaining 73% of the observed variance in decadal mean FC concentrations from a catchment scale analysis and 63% from a sub-catchment scale analysis (Fig. 2, Fig. 3a).
This is consistent with findings from catchments across the world, and with previous reach-scale findings in the Ganga catchment. However, there remains considerable variance in FC concentration unexplained by either UPD or ULD, particularly at high population densities, >100 people/km2 (Fig. 2, Fig. 3). Previous reach-scale studies did not account for the upstream boundary condition either in terms of FC flux or upstream population. These studies implicitly assumed that point sources proximal to sample sites dominated the FC signal (perhaps due to coliform die-off in transit). However, while many of our sites near larger settlements have high coliform concentrations, these concentrations are better explained by upstream population density (r2 > 0.7) than population of the nearest settlement (r2 = 0.25). Examining paired samples above and below settlements suggests that, in some cases, positive residuals (where FC concentration is greater than predicted) may reflect sites immediately downstream of population centres. However, including a distance-decay function in our analysis did not improve our ability to predict FC concentrations. Fig. 4 shows that model performance is initially stable as the rate at which FCs decay with distance increases, but that the performance is never better than that without distance decay, and that performance declines markedly for decay rates greater than 0.01%/km. This reduction in performance relates to a reduction in decay-adjusted population density, primarily at sites with intermediate or dense populations (Fig. 5). These results suggest that, UPD is an important but not singular factor in defining the connectivity between sources and receiving waters that defines the timescales and thus efficiency of delivery. 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.
Implications of the FC-UPD relationship
The increase in per capita impact as UPD increases likely reflects an increase in the efficiency of delivery rather than FC production, 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 (Census of India (2011b)) 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. This problem has historically confronted communities across the world, leading to degradation of sanitary conditions and construction of sewers. Sewage systems vary in sophistication but generally involve the movement of excreta by water out of the population centre, often made possible by piped domestic water. 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. Here we demonstrate as others have 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 alternatives as well as a growing recognition that this is a social as well as physical or technical issue.
“this model predicts that 33–48% of rivers in the Ganga catchment fail the Indian Government’s safe bathing standards”
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 good evidence to suggest that open defecation is extremely problematic for public health and safety.
The relationship between upstream population density and FC concentration enables a simple predictive relationship, albeit with considerable scatter. This model predicts that 33–48% of rivers in the Ganga catchment fail the Indian Government’s safe bathing standards, depending on the choice of standard (Fig. 6). However, many of those rivers that pass are in sparsely populated headwaters. 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.
Interventions high up the river network have the highest potential for impacting FC concentration for a given FC flux reduction because: 1) lower discharge on these rivers means that the same FC flux reduction will lead to a larger concentration reduction; and 2) 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. Decisions about what to do where are difficult, but the findings of this study can help guide strategic investment in pollution reduction.
The rivers of the Ganga catchment are subject to widespread and, in places, severe microbial pollution. Between 52–67% of measured sites fall below the Indian Government’s upper and desirable limits for safe bathing; and for 61–70% of the population the model results suggest that their nearest river falls below these same bathing standards. The network structure of the Ganga catchment pre-conditions certain rivers to be highly polluted, and others (with large Himalayan headwaters) to be more robust against pollution, despite their location on the densely populated plains. The entire population upstream (not only those nearby) contribute to microbial river pollution but urban populations contribute more pollution per capita than rural populations.
Densely populated areas dominate surface water pollution in the Ganga catchment not only because they contain many people but because their faeces are more efficiently delivered to the river network. We suggest that this increasing efficiency reflects: the transmission speed of urban sewerage systems, delivering the coliforms to the river more quickly with less die-off; and the limited capacity for sewage treatment within these systems. Addressing this problem requires investment in both sewage removal and treatment whether by increasing existing sewerage capacity or implementing decentralised treatment solutions.
Durham is one of the world’s leading universities as shown by its World Top 100 position in the QS World University Rankings 2019, where it is ranked 74th.
Analysing Water Quality
An Article by David Milledge et al, Durham Univers...
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