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NEWS AND VIEWS
16 June 2021

Most rivers and streams run dry every year

A model of the world’s rivers and streams has been developed to predict which of these watercourses flow all year round and which go dry. The analysis shows that rivers and streams that run dry are ubiquitous throughout the world.

Kristin L. Jaeger

 ORCID: http://orcid.org/0000-0002-1209-8506

0

Kristin L. Jaeger

Kristin L. Jaeger is at the Washington Water Science Center, US Geological Survey, Tacoma, Washington 98402, USA.

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The flowing waters of surface rivers and streams efficiently transport sediment, organic material and nutrients, among other things, from hillsides and overland areas to downstream lakes, reservoirs and the ocean. Along the way, rivers and streams (hereafter referred to collectively as streams) provide important resources for our communities and support rich, complex ecosystems. Non-perennial streams, which do not flow year-round, are crucial in this context. However, because non-perennial streams are less reliable sources of surface water than perennial ones, they are less-well studied than their perennial counterparts. Writing in Nature, Messager et al.1 provide a much-needed estimate of the total proportion of the world’s stream network, by length, that is non-perennial — and find that most fall into this category.
Read the paper: Global prevalence of non-perennial rivers and streams
Messager and colleagues combined streamflow data from sites around the world with information describing the hydrology, climate, physical geography and land cover at those sites, to model the probability that water does not flow for at least one day per year. They then expanded their predictions to all stream segments recorded in a global stream-network database (RiverATLAS)2.The authors report that 51–60% of the world’s streams do not flow for at least one day per year, and that 44–53% of global stream length is dry for at least one month (about 30 days) each year. Their modelling shows that non-perennial streams occur in all climates and biomes on every continent (see Fig. 1 of the paper1). The model also shows that 95% of the stream network in hot, dry regions — which represent 10% of the global landmass — runs dry each year (Fig. 1). Astonishingly, even segments of major rivers, such as the Niger River in West Africa, are predicted to dry up in these arid regions. The vast prevalence of non-perennial streams in such locations highlights how even streams that do not flow continuously substantially affect water availability and water quality. The results emphasize the need for more-detailed maps of perennial and non-perennial flows at regional and local scales, and for further studies of how non-perennial streams affect overall water availability and quality.

Figure 1 | The dried-up Darling River in New South Wales, Australia, February 2020. Messager and colleagues’ analysis1 shows that most rivers and streams run dry for at least one day per year, including sections of major rivers in arid regions.Credit: Mark Evans/Getty

Small headwater streams (those that have no tributaries) make up 70–80% of stream length worldwide3, similar to the way in which the collective length of one’s fingers is much greater than the length of the palm of the hand. Messager and co-workers’ model predicts that, even in the wettest regions, such as the Amazon River basin and portions of central Africa and southeast Asia, up to 35% of these headwater streams stop flowing at some point in the year. However, it should be noted that headwater streams are monitored by relatively few stream gauges, which tend to be located on larger, perennial rivers downstream. The model might therefore provide highly uncertain estimates for the upstream regions of stream networks.Lack of streamflow data is a common problem for the modelling of headwater streams, and so data-collection efforts are being implemented to fill this knowledge gap. For example, France has developed the Observatoire National des Étiages (ONDE) network, which complements the national stream-gauging network but focuses on headwater streams. However, these programmes are costly and require considerable investment of resources.
European rivers are fragmented by many more barriers than had been recorded
Stream gauges are also scarce for non-perennial streams more generally. In Messager and colleagues’ analysis, for instance, there were no gauges in non-perennial streams in Argentina; just one in New Zealand; and 10 in the United States Pacific Northwest, out of a network of 250 gauges. To improve models that map perennial and non-perennial streams, low-cost field observations will be needed, coupled with the development of high-resolution remote-sensing technology that frequently detects — or at least predicts — surface flow in streams.Messager and co-workers’ analysis provides a robust, quantitative confirmation of the ubiquity of non-perennial rivers. Their results indicate the need for a fundamental change in the fields of river and stream science and management, in which non-perennial streams have been largely overlooked4. In arid regions, the predominance of non-perennial streams might be a major driver of water availability and quality. And in areas where services developed by humans are not readily available, ecosystem services such as flowing water in streams are used to meet basic needs and will, in part, determine the well-being and prosperity of people in that area5. The new findings therefore shine a light on the need for global accounting of both perennial and non-perennial streams.Moreover, changes in the distribution of streams can have far-reaching impacts on carbon and biogeochemical cycles at global and continental scales6, and on the survival of stream-dwelling organisms, including many endangered species7. A global benchmark of the prevalence of perennial and non-perennial streams is therefore crucial for evaluating the effects of future changes in their distribution associated with climate and land-use change. Finally, regional and local models of streams are needed, as well as better data for headwaters and non-perennial portions of the stream network, to further increase the value of global models.

Nature 594, 335-336 (2021)
doi: https://doi.org/10.1038/d41586-021-01528-4

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Many communities in Iran depend on wells for water, and are threatened by the rapid fall in the nation’s groundwater table. Credit: Mohsen Maghrebi

Water resources
16 June 2021
Iran is draining its aquifers dry

Wells are proliferating, but data from across the country show that groundwater extraction is falling.

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Iran is using more groundwater than can be naturally recharged, according to an analysis of national data. And even as more wells are tapped into the ground each year, their overall output keeps dropping.Iran’s sources of groundwater include wells, springs and underground aqueducts known as qanats. Groundwater amounts to 60% of the country’s total supply and is consumed almost entirely by agriculture.Roohollah Noori at the University of Oulu in Finland and his colleagues studied data from Iran’s national groundwater-monitoring system from between 2002 and 2015. During that period, the number of wells and other locations that tap into groundwater nearly doubled. Yet the amount of groundwater extracted declined by 18%. Nationwide, the groundwater table dropped by an average of almost half a metre per year.In many wells, the water also become significantly saltier, to the point that only salt-tolerant plants would thrive if irrigated with it. Groundwater quality improved in only a few wet regions.Water scarcity threatens the livelihoods of people across Iran as the land becomes drier.

Proc. Natl Acad. Sci. USA (2021)

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COMMENT
15 June 2021

Community–academic partnerships helped Flint through its water crisis

A city that faced a public-health emergency shows how collaborations with neighbourhood advocates can advance health equity.

E. Yvonne Lewis

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Richard C. Sadler

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E. Yvonne Lewis

E. Yvonne Lewis is founder and chief executive of the National Center for African American Health Consciousness, Flint; co-community principal investigator at the Flint Center for Health Equity Solutions; co-director of the Healthy Flint Research Coordinating Center Community Core; and director of outreach, Genesee Health Plan, Flint, Michigan, USA.

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Richard C. Sadler

Richard C. Sadler is associate professor of public health at Michigan State University, Flint, Michigan, USA.

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Residents of Flint, Michigan, attended community blood-testing events in 2016 after lead contamination was found in the city’s water supply.Credit: Brett Carlsen/Getty

Flint in Michigan is infamous for its water crisis. From 2014, the state government decided to divert the city’s water supply through ageing pipes that contained lead, a neurotoxin, making many people unwell and leading to some deaths. Residents were left searching out water that was safe for drinking, washing and bathing. Nine public officials face criminal negligence charges around wilful neglect of duty and for allegedly concealing and misrepresenting data. A US$640-million class-action lawsuit is moving its way through the courts.But Flint should be known for more than its public-health tragedy. Accounts of the crisis often cast pioneering scientists and physicians as lone heroes, assuming that those who documented the lead in the water and blood of Flint’s residents were the ones who brought officials to account. That assumption erases the work of community activists who got academics to look for lead and its damaging health effects in the first place. Flint is a working example of how community members and academics can collaborate on problems — such as how to collect data or develop robust models of health risks and injustices — and on finding solutions.Flint’s water crisis came to light because of strong research partnerships between activists, academics and other specialists. These partnerships continue to advance work that matters to the community. Efforts include identifying neighbourhood conditions (including crime levels, asthma rates and access to healthy food) and assessing projects to improve them. It requires a commitment that research does not just end up in a thesis or paper, but becomes information that is useful to community members.Here’s one example. The Genesee Health Plan is a non-profit benefit programme that provides basic health-care coverage to uninsured residents of Genesee County, which includes Flint. It was established in 2001 and is supported by property taxes. One of us (E.Y.L.) helped to provide the other (R.C.S.) with data from a sample of Genesee Health Plan enrollees to produce maps of chronic conditions. One map showed the health plan’s wide adoption in our community, and officials used it to advocate for voter support when the tax measure was renewed in 2018. This partnership was possible only because of the connections already formed between E.Y.L., who is a community organizer, and R.C.S., a geographer and public-health specialist at Michigan State University (MSU) in Flint.
The best research is produced when researchers and communities work together
Long-standing efforts to ensure Flint community members have a voice in research have gained momentum. One tangible result was the creation of the Healthy Flint Research Coordinating Center in 2016. To form the centre, E.Y.L. and another Flint resident representing community organizations joined up with six researchers — two each at MSU, the University of Michigan in Ann Arbor and the University of Michigan–Flint. It works to minimize redundant research, maximize creation of new community–academic partnerships and ensure that research receives a community ethics review.Also established in 2016 to support equitable community–academic partnerships was the Flint Center for Health Equity Solutions, funded by the US National Institutes of Health (NIH). E.Y.L. is the centre’s overall community principal investigator. Each of its four divisions and two research projects is co-directed by a community member and an academic. The divisions are: methodology (which R.C.S. directs); dissemination and implementation sciences; administrative; and consortium partners. A programme within the centre — in which people with substance-use disorders are coached by their peers — has expanded and is now supported by additional external funding.Here, we distil how we’ve made community-based research work, and provide lessons others might use.Distinct challengesEach of us experienced different challenges before we formed our community partnership, which might offer some pointers for others considering such collaborations. To that end, here, we relate our stories individually.R.C.S. writes: I grew up in Flint, and joined MSU as a faculty member in 2015. I knew that the kind of community-focused work I was most passionate about makes it harder to rack up the publications and citations required to progress in most academic institutions, which often treat these as a proxy for high-quality research.

A medical assistant checks for the presence of lead in blood samples as part of a community campaign in Flint, Michigan.Credit: Jim West/Alamy

I still worked to hit those markers, publishing more than 50 papers in 6 years. I secured several grants from agencies that fund research that has community value — including agencies in the NIH, the US Centers for Disease Control and Prevention (CDC) and the Michigan Department of Health and Human Services. My focus was on work that mattered to the community, and I didn’t worry whether journals had high impact factors or huge name recognition.The community-engaged philosophy of the College of Human Medicine at MSU — where I gained tenure this year — made it more open to alternative metrics, such as volunteering on local non-profit committees, conducting community-based mapping and talking about research at local meetings. The key was to frame my academic output on a longer time scale than that of publications — long enough to see meaningful change.
Rethink how we plan research to shrink COVID health disparities
E.Y.L. writes: As an African American female community activist of decades’ standing, I worried about being physically mistreated, emotionally abused and misrepresented by research institutions. On one occasion before I moved to Flint, I remarked that some physicians’ descriptions of pregnant African American women as unconcerned with or unwilling to take care of their own needs did not reflect people in my community. I was asked about my academic credentials and then ignored for the rest of the conversation. I experienced this often during the water crisis: community members were touted as being great citizen scientists, until there was disagreement with the ‘real scientists’. Then we were marginalized and told we lacked the necessary degrees to provide input.As community members, we also see our ideas appropriated. For instance, during a discussion at one national meeting, I made a distinction — on the basis of my own experience — between projects that were faith-based (driven by religious principles) and those that were faith-placed (using spaces such as churches). The following year, a researcher presented data based on this model without acknowledging me as the inspiration. I felt dishonoured, discouraged and demotivated. I now ask academic partners to give attribution for my ideas. Knowing the norms — and what credit to request — has helped me immeasurably.Joint challengesOne of the biggest barriers to community participation is language. Words can have different meanings in different contexts — for instance, the phrase ‘those people’ can be highly offensive in many situations. When community members hear terms such as public engagement, they assume that ‘public’ refers to a broad, mixed group of individuals, such as those who might go to a public event. Yet academics often use the term to mean targeted outreach to specific groups of people — faith leaders, patient groups or policymakers, say. And to help navigate excessive jargon in the early stages of Flint’s partnerships, one group developed a glossary of acronyms such as NIH and CDC.Importantly, everyone involved must take time to understand the culture and unique characteristics of the groups within communities. Not all Black communities are the same, for instance, and none is homogeneous. The heterogeneity among people’s levels of income, education and health insurance must be kept in mind in communications. Research materials written in English for a ‘general audience’ might not be appropriate — strong cultural dialects and a lack of access to information need to be considered.Funding norms can also become a barrier to sustaining long-term relationships. Grants that last only one, two or five years are insufficient to address many community concerns. Too many communities have experienced projects for which funding ends and researchers move on, leaving unfinished work. Without sustained effort, the situation can revert to being the same or worse than it was before the project began. This is partly why community-engaged work is so important: researchers committed to the cause will continue as partners long after the funding is gone. And if grants from typical funders run out, academics will find other sources of support for community partners — such as by maintaining relationships with local philanthropies. (In Flint, such support has come from the C. S. Mott Foundation and Community Foundation of Greater Flint.)

A local church in Flint was set up as a water distribution centre because lead contamination had made the public supply unsafe.Credit: Tom Williams/CQ Roll Call

Researchers often come to communities with a prepared study design, seeking approval rather than input — even when input could improve a study. Researchers assessing campaigns to promote healthy eating might include a control group that receives nothing, whereas the treatment group receives a suite of services and vouchers. This creates a perception of unfairness that can warp a study and discourage participation. Too often, researchers treat community partners who point out such risks as a barrier to progress, rather than as a liaison to a robust study. That attitude undermines future interactions. Establishing realistic expectations is one way to mitigate this issue.Researchers might also offer to provide training in work that is already under way. For example, Flint has a crime-reduction programme in which residents proactively assess whether street lights are working and maintain vacant properties. Proposals that disregard what is already in place are wasteful and cause resentment. At one point, a team of researchers approached us to implement a healthy-eating project, not realizing that the Flint community had helped to develop the recipe book on which it was based. The Healthy Flint Research Coordinating Center now maintains an index of projects to discourage redundant work (one of R.C.S.’s tasks).
Farmers transformed how we investigate climate
Before and especially during the water crisis, a string of ‘helicopter researchers’ from outside Flint came to study topics from environmental issues to violence. Community members were asked to fill out surveys, or learnt through informal chatter about researchers who wanted records about emergency hospitalizations. But data and insights were not brought back to the community. Many residents felt used and dismissed. The coordinating centre now works with researchers so their results can be applied to inform and improve the community where data were collected.Interactions are generative: when academic researchers dismiss community ideas, take them without credit, bristle at valid input, ‘introduce’ programmes that are already in progress or focus more on producing papers than on helping communities, residents will expect the same of other researchers. Even those with the best of intentions can be rebuffed or face distrust, something R.C.S. was attuned to when he began his transition from Flint community member to academic.Nurture relationshipsIdeally, interactions become constructive feedback loops. In 2018, E.Y.L. provided health-plan data to R.C.S.. The resulting analysis using a geographic information system (GIS) showed, for the first time, that the centre of Flint was an asthma hotspot (see ‘Asthma hotspots in Flint’). This pattern correlates with historical sites of car factories and lead contamination in the soil (M. A. S. Laidlaw et al. Int. J. Environ. Res. Public Health 13, 358; 2016). R.C.S. explored how best to show those patterns in ways that would be interpretable and helpful to community members. These results have informed targeted outreach activities, such as developing tailored materials based on local landmarks and identifying specific neighbourhoods, churches or community groups where the materials can be distributed.

Source: E. Yvonne Lewis & Richard C. Sadler

None of this would have happened without the partnership and trust we had built. The university needed access to health-plan data. Health-plan officials had to trust researchers to answer relevant questions, honour patient confidentiality and provide insight to accomplish the plan’s goal.As the value of such analysis became clear, community members were eager for more. Most neighbourhood and community groups come together to solve a specific, immediate problem, not to form a self-sustaining, long-lasting organization, so they rarely consider mechanisms for collecting long-term data. Flint now sees community members approaching researchers; they seek to evaluate programmes that they’ve put into place. They want data to support the fact that they do good work and to show which efforts are most effective. A true partnership has been achieved.The partnership represents many works in progress, far beyond what we describe here. There are still conflicts, miscommunication and lost opportunities. But we now know how to set ourselves up for success as projects emerge.The most important ingredient in making collaborations work is commitment: to producing research that is relevant, and to understanding many angles and perspectives. This means spending less time and attention on conventional metrics, such as published papers, journal impact factors and procured grants, and much more on nurturing relationships. In true community-based partnerships, a paper is incomplete without a link back to the local community.Although our experiences are specific to Flint, community–academic partnerships that focus on research that is relevant to policy are essential worldwide. Regions in the Rust Belt of North America, Eastern Europe and east Asia have all experienced population decline and economic problems. More will soon do so. Exploring solutions is of benefit both to researchers and to communities when they work together.

Nature 594, 326-329 (2021)
doi: https://doi.org/10.1038/d41586-021-01586-8

Competing Interests
The authors declare no competing interests.

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Start-up phaseTo avoid the toxic effect of higher nitrite concentration on microorganisms30, the influent NH4+–N and NO2−–N during the first 66 days was maintained at 50 and 60 mg/L, respectively. The NH4+–N removal efficiency in each reactor reached up to 60% quickly with a certain amount of NO3−–N in the effluent. On the 5th day, the NH4+–N removal from reactors R1–R4 was 32.1, 36.9, 40.4, and 39.1 mg/L (Fig. 1a), respectively cumulative concentration of NO3−–N was 23.7, 33.3, 39.9, and 31.1 mg/L in respective reactors (Fig. 1c). The higher concentration of NO3−–N might be due to the proliferation of nitrifying bacteria or lower denitrifying bacteria activity. It has also been reported that other bacteria, despite nitrite-oxidizing bacteria (NOB), carrying nxr gene, can contribute to nitrate production from nitrite in an anammox reactor31. Besides, concentrations of NO2−–N in each reactor’s effluent showed a different degree of an increasing trend. On the 11th day, the NO2−–N concentration in the effluent of R1–R4 reached 60.5, 52.6, 62.8, and 54.1 mg/L, respectively (Fig. 1b).Fig. 1: Nitrogen removal performance of four reactors during the start-up period.a NH4+–N concentration (mg/L), b NO2−–N concentration (mg/L), c NO3−–N concentration (mg/L).Full size imageFurthermore, the DO of each reactor was also monitored, and it was found that DO values of four reactors in the first 20 days were varied from 0.20 to 0.50 mg/L. The oxygen half-saturation constant of AOB has been reported in the range of 0.2–0.4 mg/L, and the growth rate of AOB can reach 0.5–1.0 per day32. Thus, AOB and archaea, and some NOB adapt to the lower DO concentration and could grow33,34,35. So, the higher NO3−–N values in the start-up phase can be justified by the growth of nitrifying bacteria and some other bacteria carrying the nxr gene. However, as the incubation time increased, some bacteria which may not be adapted to the conditions might be degraded. The denitrifying bacteria could propagate on the carbon source provided by degraded biomass12. The use of NOx−–N by denitrifying bacteria contributed to the nitrogen removal rate (Figs. 1 and 2c) and can be called denitrifying propagation phase. Due to unfavorable environmental conditions, the degraded bacteria could have provided a carbon source for denitrification bacteria’s growth13,14. It has been seen from Fig. 2c that in the initial, nitrogen removal efficiency showed a decreasing trend, which is caused by consumption of endogenous organic matter, which slowly consumed and excluded the dominancy of the denitrification process. The conditions in the reactors are beneficial to anammox growth and eventually led to the washout of denitrifiers. The high TN removal in the latter phase was attributed to the enrichment of anammox abundance.Fig. 2: Nitrogen removal performance of the four reactors in the stabilization period.a NH4+–N concentration (mg/L), b NO2−–N concentration (mg/L), c removal efficiency (%) of total nitrogen.Full size imageFrom the 29th day, the effluent concentrations of NH4+–N and NO2−–N in R3 declined sharply, and the NO2−–N and NH4+–N removal ratio increased from 0.52 to 1.24, close to the theoretical ratio of 1.32, which indicates a rapid increase in the anammox bacterial activity5,36. The stoichiometric ratios of R1, R2, and R4 reached close to theoretical value on days 50, 50, and 41, respectively. The activity enhancing phase started around 22 days earlier in R3 when compared with R1, which clearly showed the positive effects of MF to short the start-up period of anammox. Though R4 showed a more extended start-up period than R3, it was still shorter when compared with R1. It was observed that R2 with the only nZVI did not show a clear difference from the control reactor. On the 34th day, the NH4+–N and NO2−–N removal rates of R3 were 95.5% and 88.1%, respectively, which maintained at 85% from days 41 to 60. Compared with the control, the start-up time of R3 was shortened from 60 days to 34 days under the action of MF, and the efficiency was increased by 43.3% in the start-up period. The start-up time of the reactor under the combined effects of nZVI and MF was ~50 days, and the efficiency increased by 16.7%. On the other hand, the start-up time of anammox in R1 and R2 showed no noticeable difference.Reactors’ performance under increased nitrogen loadingAfter the successful start-up of all reactors, the influent NH4+–N and NO2−–N concentrations were increased progressively to observe the reactors’ stability under different loading rates. As shown in Fig. 2, on day 69, the influent concentrations of NH4+–N and NO2−–N were increased from 50 to 100 mg/L and 60 to 120 mg/L (nitrogen loading rate was 2.64 kg/L/d), respectively. The response of R2 against increased N loading was almost comparable to R1. The concentration of effluent NO2−–N in R1–R3 was slightly increased. The removal performance of R4 was somewhat better than R1. Similarly, the removal of NH4+–N and NO2−–N in R4 was better than R3 (Fig. 2a, b). This implies that the increase in nitrogen loading does not have a deterioration impact on the operation of the four reactors after the successful build-up of anammox activity.The influent concentration of NH4+–N and NO2−–N was again amplified to 200 and 240 mg/L (nitrogen loading rate was 5.28 kg/m3/d) on the 91st day. After the second increment in N loading, the effluent NH4+–N concentration in R1 and R4 was increased to 69.2 and 56.3 mg/L, respectively, and the effluent NO2−–N concentration was increased to 55.5 and 37.4 mg/L in R1 and R4, respectively. The increasing trend in effluent NO2−–N concentration in R1 was observed till the 106th day, and the highest value of effluent NO2−–N concentration was 134.8 mg/L which decreased the removal rate in R1 about 42.4%. Likewise, considerable fluctuations were also observed in the removal rate of NH4+–N, where more variation was noted in R1. A little higher nitrogen removal efficiency of R3 on day 91 (80%) and on day 106 (71.18%) were witnessed compared to R4 (67% and 66%, respectively). Similarly, a little high nitrogen removal efficiency was registered in R2 on days 88–91 and on days 97–100 as compared to R4. These variations might be caused due to the calculation error. The presence of MF and nZVI decreased the influence of substrate shock on the anammox performance. Although the removal rates of NH4+–N and NO2−–N in R4 were also decreased, but the fluctuation was slighter than R1 and R3. On the 115th day, the removal rates of NH4+–N and NO2−–N in R4 were recovered to over 80% and gradually reached over 90% afterward. Comparable results have also been observed by Wang et al.14 in ABBR. However, Chen et al.6 reported different results, which showed anammox was failed to adapt to the higher nitrogen loading rate. In addition, the increase in nitrogen loading also showed a negative impact on the nitrogen removal performance of R3. As shown in Fig. 2c, total nitrogen removal efficiency (about 80%) of R4 was always better than other reactors from the 90th to 180th day.Fig. 3: Nitrogen mass balance of R4 on day 166.This mass balance is drawn on the base of anammox reaction stoichiometry. The black arrows showed the amount of nitrogen to effluent, the red lines indicated anammox process, the green line indicated partial nitrification process, and the pink arrows indicate partial denitrification or partial-DNRA process.Full size imageNitrogen mass balance (Fig. 3) on day 166 proved the coupling of nitrogen removal bacteria on the basis of reported anammox stoichiometric values of NO2−–N/NH4+–N (1.32) and NO3−–N/NH4+–N (0.26). So, if there is only an anammox process, ammonium and nitrite consumption should be around 196 and 258 mg/L, respectively, to meet the reported stoichiometry of the anammox process and effluent nitrate concentration should be approximately 51 mg/L. However, the stoichiometric values of NO2−–N/NH4+–N and NO3−–N/NH4+–N obtained in this study were around 1.15 and 0.19, respectively, in R4 (coupled effects of nZVI and MF) on day 166. This means, 227 mg/L of NO2−–N and 196 mg/L of NH4+–N consumed by anammox, and the remaining amount is removed by other nitrogen cycle bacteria. The NH4+, NO2−, and NO3− attributed to various groups of microorganisms by keeping R1 (1.15) and R2 (0.19) values by using Eqs. 4 and 5. Overall, the proposed nitrogen mass balance shows a clear coupling of anammox with other nitrogen cycle bacteriaEffects of nZVI and MF on the functional genesSo as to study the combined effect of nZVI and MF on functional genes of anammox bacteria, the 16S rRNA gene copy numbers of anammox and other nitrogen cycle bacteria at different stages of the experiment were determined by the qPCR technique. The copy numbers of hzo (hydrazine oxidoreductase) functional gene were also recorded.The anammox 16S rRNA copy number was increased gradually with time (Fig. 4a). Initially, the copy number of anammox 16S rRNA was 1.01 × 106 copies/ng DNA. R1 had the lowest anammox copy number among the four reactors on the 60th day, 120th, and 180th day. The highest copy number of anammox 16S rRNA was recorded in R4 (2.13 × 106), which was followed by R3 (2.10 × 106) and R2 (1.70 × 106) on the 60th day. Nevertheless, R4 and R3 have almost similar gene copy number on the 60th day, which made us propose that MF positively influence the anammox activity, which reduced the start-up period of anammox. It has been reported that bacterial activity is suppressed in the presence of incompatible nZVI concentrations37,38,39. The adaptive capacity of microbes depends on the nZVI concentration40. The inhibition in anammox activity at higher nZVI concentration (3 g/L in this experiment) is also supported by a comparable study41. Interestingly, at day 180, the copy numbers of anammox 16S rRNA in R2 (7.17 × 106 copies/ng of DNA), R3 (6.98 × 106 copies/ng of DNA), and R4 (7.8 × 106 copies/ng of DNA) were significantly higher than R1 (5.56 × 106 copies/ng of DNA). It has also been claimed that the optimum concentration of nZVI can improve the proliferation of anammox cells after adaptation41. It is reported that nZVI lost the reactivity after 3 and 60 days in the presence and absence of oxygen in the wastewater treatment system, respectively42. It is an accepted fact the nZVI released Fe2+ and H2 in the solution43, which can be stored by anammox. Anammox can store iron ions for future haem synthesis and haem-containing enzymes involved in the electron transport chain44. Further, according to the previous study, the addition of nZVI enhanced the abundance of anammox bacteria22,23,45. The above explanation supports the conclusion about the higher anammox gene copy number in R2 in the later stage. Compared with R1, the percent increase of anammox gene copy numbers under a higher nitrogen loading rate were 29.0%, 25.5%, and 40.3% in R2–R4, respectively, on day 180.Fig. 4: The qPCR results of anammox enrichment process.a Anammox 16S rRNA copy numbers at different days (0, 60, 120, and 180) and b functional gene copy numbers of AOB, denitrifying bacterial, and anammox on day 180. Data indicate average, and error bars represent standard deviation of the results from three independent samplings, each tested in triplicate.Full size imageFurther investigation was done to analyze the functional gene hzo of anammox and other nitrogen-cycle related bacteria in the reactors (Fig. 4b) on day 180. The co-existence of AOB and denitrifying bacteria with anammox is reported to improve nitrogen removal efficiency46. The copy number of hzo gene in R1–R4 were 2.39 × 105 and 3.87 × 105, 3.29 × 105, and 4.01 × 105 copies/ng DNA, respectively. The coupled effects of nZVI and MF on anammox functional gene copy number were obvious, which might be responsible for the higher nitrogen removal under increasing nitrogen loading. The contents of amoA, nirK, and nirS in R3 were less, different from other reactors. The difference in amoA, nirK, and nirS gene copy numbers implies that microbes behave differently under the influence of MF. The optimum range of MF intensity is dissimilar for different microorganisms28,47, which can be considered a possible reason for the difference in gene contents among all four reactors. The lower nirK and nirS gene contents in R3 indicated that MF of this intensity (65 ± 10 mT) might have inhibitory effects on denitrifying bacteria. Furthermore, higher nirK gene contents in R4 (nZVI + MF) originate a premise that nZVI might have neutralized the adverse effects of MF on denitrifying bacteria. As it can be seen from Fig. 4b, the denitrifying functional genes nirK and nirS were higher in nZVI reactor. Further, the lower amoA gene content signifies anammox and denitrifying bacteria as major contributors in nitrogen removal.Effects of nZVI and MF on microbial communitiesIt is an established fact that community structure and abundance considerably affect the stability and performance of the anammox process. Therefore, the community composition of the four reactors was analyzed after 180 days by high-throughput sequencing.The sludge samples from all four reactors were collected at the end of the experiment, and operational taxonomic units (OTUs) and four indices (ACE, Chao1, Simpson, and Shannon) of each sample were calculated (Table 1). The OTUs varied from 1599 to 1889 for different treatments. The ACE and Chao1 indices reflect the community’s richness, while Simpson and Shannon’s indices reflect the community’s diversity48. Generally speaking, the greater the Chao1 or ACE index, the higher the abundance of the community. The Shannon diversity index comprehensively considers the richness and uniformity of the community. The higher the Shannon index value, the higher the diversity of the community. The Simpson index is also one of the commonly used indexes for evaluating community diversity. The higher the Simpson index value, the higher the community diversity. The value of ACE (1600) and Chao1 (1600) indices of R1 was the minimum in four reactors reflecting the lowest community richness in R1. On the other hand, the maximum community richness was observed in R3 (MF reactor) as reflected by the highest value of ACE (2180) and Chao1 (2145) indices followed by R2 (nZVI reactor) and R4 (nZVI + MF reactor). In short, the effect of MF on community richness was more pronounced than the effect of nZVI and the combination of nZVI and MF. The Simpson indices of all samples were not different. However, the Shannon indices of R4 were slightly higher than other samples, which showed a little higher diversity of community than other reactors.Table 1 The OTU numbers and bacterial diversity indices of sludge samples.Full size tableThe effects of nZVI and MF treatments on community structure distribution of sludge samples at different classification levels are presented in Fig. 5. The Chloroflexi, Proteobacteria, Cholorobi, and Planctomycetes were the abundant phyla in all samples with little variation among different treatments (Fig. 5a). The presence of phyla such as Chloroflexi, Proteobacteria, and Cholorobi in an anammox reactor was also reported previously49. Relative abundance of Chloroflexi in R1–R4 was 24.3%, 29.5%, 23.4% and 30.0%, respectively. The phylum Chloroflexi has also been detected in the anammox reactor and single-step autotrophic nitrogen removal system50,51. Some genera’s proposed role belongs to Chloroflexi in the anammox reactor is to consume the dead organic material and avoid their accumulation52. The relative abundance of Proteobacteria in R1–R4 was 25.2%, 28.5%, 22.5%, and 27.5%, respectively. According to the previous literature, mostly nitrifying and denitrifying bacteria (Thauera, Denitratisoma, and Geobacter) belong to the phylum Proteobacteria and could use NO2+–N and NH4+–N for their metabolism and proliferation activities4. As far as the relative abundance of phylum Planctomycetes in different reactors is concerned, no significant difference in R1 (8.4%), R3 (8.4%), and R4 (8.5%) was noted in this regard (chi test, p  More

The global databaseAs of 10 December 2019 a total of 9503 HAEDAT events had been entered from across the globe, comprising 48% seafood biotoxin, 43% high phytoplankton counts and/or water discolorations causing a socio-economic impact, 7% mass animal or plant mortalities and 2% others (including foam and mucilage production). In a number of HAEDAT records, a single incident was categorised into multiple event types, such as both water discoloration and high phytoplankton count (11% were multiple event types). Among all events linked to seafood toxin syndromes, Paralytic Shellfish Toxins (PST) accounted for 35%, Diarrhetic Shellfish Toxins (DST) 30%, Ciguatera Poisoning (CP) and marine and brackish water cyanobacterial toxins each 9%, Amnesic Shellfish Toxins (AST) 7% and others 10% (including Neurotoxic Shellfish Toxins (NST), Azaspiracid Shellfish Toxins (AZA) and toxic aerosols).Different OBIS regions (Fig. 1A) contained varying numbers of HAEDAT reports, with the largest number of records available for Europe, followed in order by North Asia, Mediterranean, the east and west coast of North America, the Caribbean, Pacific/Oceania, South East Asia and more limited data sets for South America, and Australia/New Zealand (Supplementary Table 1).All geographic regions were impacted by multiple HAB types, but in varying proportions (Fig. 1B). High phytoplankton density problems comprised more than 50% of regional HAEDAT records in the Caribbean, Benguela, Mediterranean Sea, North and South East Asia, while seafood toxins and fish kill impacts dominated in all other regions. The productive Benguela upwelling system is prone to mass benthic mortalities linked to high algal biomass and low oxygen19. Among toxin-related impacts, those caused by PST prevailed in North America, the Caribbean, South America, South East Asia and North Asia, whereas DST were the most frequently recorded in Europe and the Mediterranean (Fig. 1C), and are an emerging threat in the USA. NST were confined to Florida (US), with a single outbreak also reported from New Zealand. Human poisonings from Ciguatera were prominent in the tropical Pacific, the Indian Ocean, Australia and the Caribbean. While HAEDAT records of PST, DST and AST mostly relate to the incidence of phycotoxins in seafood, causing closures of shellfish harvesting areas but rarely being associated with human poisonings, records for Ciguatera (CP) refer almost exclusively to human poisonings reported by medical practitioners. HAB events related to marine cyanotoxins were mainly reported from Australia/New Zealand (ANZ), the Indian Ocean (IND) and the Baltic Sea area in NW Europe (EUR).Temporal trends in HAB eventsThe number of HAEDAT events reported for each region per year generally showed increases (Fig. 2A). Specifically, eight of the nine regions showed increases of which six were statistically significant (ECA, CCA, WCA, SEA, MED, EUR; Supplementary Table 1). The meta-analysis of the relationship between HAEDAT events and Year, plotted for each region with the significance level and the confidence intervals adjusted by the effective degrees of freedom (reduced because of autocorrelation) is shown in Fig. 2B. Most of the correlations for the individual regions overlapped zero (i.e. they were not significant), but the overall global total meta-analytic correlation was significant (r = 0.37, z = 2.97, p = 0.003) because it combined the information from each individual region and thus provided more statistical power. This suggests that the number of HAEDAT events is increasing over time. The meta-analysis of the relationship between number of geographic grids with one or more HAB events and Year had similar results but weaker statistical significance (r = 0.27, z = 2.16, p = 0.031, Fig. 2C).While the number of geographic grids with HAB events is less prone to inconsistencies in what constitutes a HAB event and less affected by sampling effort, to try and adjust more specifically for sampling effort we used OBIS data on microalgae sampling. OBIS data generally showed an increase in sampling effort, although the SAM and NAS regions did not follow this trend (Fig. 3A). Once HAEDAT events were adjusted relative to OBIS observations, there were contrasting trends in standardised HAB events over time, with four regions (SAM, WCA, ANZ and NAS) changing direction compared to the unadjusted HAEDAT data (Fig. 3B vs. Fig. 3A). The meta-analysis of the standardised HAB events showed five of the nine regions with a substantially increasing trend (two flat, and two declining), but there was no statistically significant trend overall when all regions were combined (r = 0.35, z = 1.33, p = 0.18), (Fig. 3C). This implies there is insufficient evidence to conclude that HABs are increasing across all the regions analysed, but it is clear there are contrasting trends in individual regions.In the period studied, aquaculture production increased 16-fold from a global total 11.35M tonnes in 1985 up to 178.5M tonnes in 2018, with the largest increases occurring SEA and SAM + CCA and with North America (ECA + WCA) and EUR stabilising (Fig. 4A). The number of HAEDAT events over time was significantly correlated with aquaculture production, with all regions with suitable data exhibiting more HAEDAT events as aquaculture expanded, with a strongly significant relationship overall (r = 0.43, z = 3.59, p = 0.0003; Fig. 4B bottom; Supplementary Table 2).Fig. 4: Changes in different geographic regions of aquaculture production in the period 1985 to 2018, and meta-analysis of HAEDAT events over time against aquaculture.A Changes in 1985 to 2018 in five regions (ECA + WCA; SAM + CCA; ANZ; SEA; EUR) of tonnage of Aquaculture Production of fish, molluscs, crustaceans and aquatic plants; and B. Meta-analysis of HAEDAT events over time vs. Aquaculture. The overall number of HAEDAT events over time was significantly correlated with aquaculture production (bottom). Weighted mean correlations (filled circles) are shown with 99% confidence limits (bars) in (B).Full size imageSelected HAB case studiesFurther exploring the influence of monitoring efforts, Fig. 5 depicts a 4× fold increase of positive global records between 1985 and 2018 of the main causative organisms of Diarrhetic Shellfish Poisoning (DSP; 84,392 OBIS records of the dinoflagellate genus Dinophysis; Fig. 5A), a 7× fold increase of global observations of the main causative organisms of Amnesic Shellfish Poisoning (ASP; 128,282 records of the diatom genus Pseudo-nitzschia; Fig. 5B) and 6× fold increase of global observations of one of the causative organisms of Paralytic Shellfish Poisoning (PSP; 9887 records of the dinoflagellate genus Alexandrium; Fig. 5C). It should be noted that records for Dinophysis, Pseudo-nitzschia and Alexandrium may also include non-toxic species or strains. In all three cases the clear increase in the number of observations through time of causative organisms is paralleled by the increase of HAEDAT records of the associated toxin syndromes (Fig. 5D–F) which in the case of PSP are contributed also by other species, namely the tropical Pyrodinium bahamense and widespread Gymnodinium catenatum. The occurrence of the causative toxigenic HAB species is not always an accurate predictor for the incidence of human shellfish poisonings (indicated by the shellfish icons in Fig. 5G–I). This reflects the efficiency of the food safety risk management strategies implemented in many of the affected countries. Globally, some 11,000 cases of nonfatal events related to DSP were reported, mostly from Europe, South America and Japan (Fig. 5G). These events mainly include closures of shellfish harvesting areas due to observed levels of DST above regulatory limits to protect human health. It is noted that, despite the widespread distribution of Pseudo-nitzschia species (Fig. 5H), there have been no human fatalities from Amnesic Shellfish Poisoning since the original 1987 incident in Prince Edward Island, Canada (150 illnesses with 3 fatalities), even though associated mortalities of marine mammals of high conservation value are of increasing concern in western North America, including in the climate hotspot of Arctic Alaska36. AST has also been associated with marine mammal calf mortalities in Argentina37. Of the global total of 3800 human Paralytic Shellfish Poisonings during the 1985–2018 period (Fig. 5I), the largest number occurred in the Philippines, a country strongly dependent on aquaculture for human food protein, with 2555 poisonings recorded between 1983 and 2013 of which 165 were fatalities23,38, predominantly caused by highly toxic Pyrodinium bahamense. Because of the increased use of molecular detection methods our knowledge on the global distribution of ciguatera- causing organisms, selected species of the benthic dinoflagellates Gambierdiscus and Fukuyoa, has increased considerably (Fig. 6A). The database on the presence of ciguatoxins in fish (Fig. 6B) is still limited because of the complexity of the chemical analysis used to confirm the presence of ciguatoxins. Exploring trends of human CP, in Hawaii poisonings have been decreasing, in French Polynesia and the Caribbean numbers remained stable, whereas CP is a new phenomenon in the Canary Islands (Fig. 6C). Globally CP affects 10,000–50,000 people annually but fatalities are rare20.Fig. 5: Increases between 1985 and 2018 of global observations of the causative organisms, HAEDAT toxic events, and distributions of the toxin syndromes Diarrhetic, Amnesic and Paralytic Shellfish Poisoning.A–C Total number of global observations from OBIS of causative microalgal organisms of Dinophysis spp., Pseudo-nitzschia spp. and Alexandrium spp.; D–F The number of records of HAEDAT Toxic Events of DST, AST and PST.; G–I Global distribution maps (as red dots, from OBIS) as of 2018 of Dinophysis spp. (DSP), Pseudo-nitzschia spp. (ASP) and Alexandrium, Pyrodinium, Gymnodinium catenatum (PSP). The locations of toxic events resulting in human poisonings are indicated by the size of the shellfish icons. The first number shows number of poisonings, the second number indicates fatalities. For ASP, 150/3 signals 150 clinical cases with three fatalities. No human fatalities have ever occurred from DSP.Full size imageFig. 6: Known global distribution in 2018 of the causative dinoflagellate genera, ciguatoxins in fish, and trends of human ciguatera poisonings in selected geographic regions.A Distribution of the dinoflagellate genera Gambierdiscus and Fukuyoa (blue and orange dots); B Ciguatoxins in fish (red) and shellfish (orange); and C Trends between 2000 and 2018 in human ciguatera poisonings in Hawaii, French Polynesia, Canary Islands, the Caribbean (light green) and Mexico (dark green). Adapted from Chinain et al.20.Full size imageAquacultured finfish mortalities caused by the taxonomically unrelated microalgal genera Chattonella, Pseudochattonella, Heterosigma, Karenia, Karlodinium, Margalefidinium (Cochlodinium) and Prymnesium/ Chrysochromulina globally account for much greater economic damage than HABs contaminating seafood39. While most shellfish toxins have now been well characterised and are effectively monitored and regulated, finfish held captive in intensive aquaculture operations continue to be vulnerable to HABs (USD71M loss in Japan in 1972, USD70M in Korea in 1995, USD290M in China in 2012, USD100M in Norway in 201919,20,21,22,23,24,25,26,27,28,29,30,31,32), even though the causative ichthyotoxins usually are of no human health significance. The 2016 Chilean salmon mortality that caused a record USD800M loss led to major social unrest40. Again, the incidence of fish-killing HAB species is not an accurate predictor of economic losses. For example, Heterosigma blooms occur both on the west and east coast of North America, but fish mortalities are mostly confined to the west coast29. In large part, this reflects locations where blooms occur relative to the location and size of the aquaculture operations. The dinoflagellate Karlodinium australe never caused any problems in its Australian lagoon type locality41 but in 2014 killed 50,000 caged fish in Malaysia and is now also known from Japan and the Philippines42. In the wild, finfish can swim away from bloom areas, hence aquaculture finfish mortality is largely a human-generated problem. More

Monday is sample-collection day in Cape Town, South Africa, and Aqeelah Benjamin is halfway through her shift. At the Green Point wastewater-treatment plant, under the Atlantic coast promenade, she fills a 500-millilitre bottle from a tap of untreated water. She wipes the bottle’s exterior, cleans it with a spritz of ethanol and stores it on ice.It’s one of nine samples that Benjamin will collect today, each from a different facility. Later, she’ll drop them off at the South African Medical Research Council (SAMRC) laboratory, where they will be tested for the presence of SARS‑CoV-2, the virus that causes COVID‑19. But that’s just a fraction of the samples’ potential — waste water contains a wealth of information about public health, and scientists are only just starting to tap into its potential.Wastewater testing can provide an unbiased snapshot of community health: whatever access they have to the health-care system, everyone has to go to the toilet. And as the effluent makes its way to treatment facilities, researchers can test it to determine what pathogens might be present. For more than 40 years, researchers have used waste water to monitor the spread of poliovirus. Norovirus, influenza, hepatitis and measles viruses can also be found in waste water. Now, a growing number of countries are using waste water to monitor the spread of COVID-19. The memorably named COVIDPoops19 Dashboard, run by researchers at the University of California, Merced, lists more than 2,200 monitoring sites in 54 countries (see go.nature.com/3fjfcjt).South Africa is one of a handful of countries rolling out the technology nationwide. But the process isn’t easy: researchers are struggling to overcome logistical hurdles and extend the techniques to the large part of the population that has no sewerage infrastructure.
The myriad ways sewage surveillance is helping fight COVID around the world
The SAMRC runs a research programme across four of South Africa’s nine provinces — the Western Cape, home of Cape Town; the rural Eastern Cape and Limpopo; and Gauteng, which includes South Africa’s largest city, Johannesburg, and its administrative capital, Pretoria. After showing that it could detect SARS-CoV-2 in waste water in 5 treatment plants last June, the SAMRC extended the testing to another 19 plants to work out the logistics of scaling up the work.The exercise highlighted problems specific to operating in South Africa. Difficulties collecting samples from remote sites can slow down the diagnostic process, for instance. And rolling electricity blackouts, known locally as ‘load shedding’, can hinder the operation of the equipment that samples waste water throughout the day. These machines, known as composite samplers, are also prone to theft. Considering this, and the cost of the samplers, South African researchers tend to use ‘grab samples’, such as Benjamin’s. These samples are generally considered less representative than are those from composite samplers, because they represent just a single snapshot in time. But a study by the South African Water Research Commission (WRC) has found little difference in the effectiveness of the two approaches in detecting SARS-CoV-2 (see go.nature.com/3v1mpm4).On Monday afternoon, after Benjamin and two other collectors deliver their samples to the SAMRC lab in the northern Cape Town suburb of Parow, I meet Rabia Johnson, deputy director at the SAMRC’s Biomedical Research and Innovation Platform. The lab specializes in molecular biology and cell-culture systems, and has been testing waste water for SARS-CoV-2 for nearly a year. “I think we’ve got the most comprehensive longitudinal database [in South Africa], from the first wave through the second wave,” Johnson says.

Researchers process samples for testing at their lab in Tshwane.Credit: Delwyn Verasamy/Mail & Guardian

In the lab upstairs from Johnson’s office, the researchers concentrate the samples in a centrifuge and then extract any viral RNA using a kit from the molecular-reagents company Qiagen in Hilden, Germany. The kit is optimized for extracting RNA from soil rather than water, but researchers at Yale University in New Haven, Connecticut, have shown that it is better at handling the unwanted organic materials found in wastewater than are conventional techniques (J. Peccia et al. Preprint at medRxiv https://doi.org/gc9k; 2020). The team then moves the extracted RNA to a ‘clean’ room to test for SARS-CoV-2 to avoid the risk of contamination. The researchers use a technique called real-time quantitative polymerase chain reaction (RT-qPCR) to quantify the amount of RNA that encodes the viral nucleocapsid protein. Other viral sequences are added in to assess performance. And positive controls are added for two key variants: 501Y.V2, first identified in South Africa, and B.1.1.7, detected in the United Kingdom. A fluorescent signal indicates that the nucleocapsid RNA is present.Finally, Johnson cleans up the data and sends them to the SAMRC’s Environment and Health Resource Unit. Researchers there upload the data to the SAMRC dashboard, a public resource launched in November 2020 that plots virus spread on a map (see go.nature.com/3ukn74u). Around 700 people per week access the service, according to Renée Street, a senior scientist at the unit.Early warning systemBecause wastewater testing can capture the presence of the virus 7–14 days before the onset of symptoms, it can provide valuable early warning of localized outbreaks. Health officials can then make sure the necessary resources, equipment and personal protective equipment are available, says Johnson.
How sewage could reveal true scale of coronavirus outbreak
But that’s still theoretical. Wastewater data have not been used directly to inform decisions about control measures such as targeted lockdowns or resource allocation in South Africa, but they have been used alongside other sources of information, including case numbers and hospital admissions. “It’s still very new science,” says Street. “We’re still working out what the different signals are, and at what signal level we would need to take action.”The ability of the technology to identify hotspots is governed by the service area of the treatment plant: the wider the spread, the harder it is to pinpoint small outbreaks, and at least one facility serves more than 850,000 people. But Cape Town epidemiologist Natacha Berkowitz, says that the goal is to “localize infection down to a small unit area, like a suburb”.After the pandemic, the city plans to use waste water to regularly monitor for polio and other viruses. Although South Africa has been declared polio-free, missed vaccinations during the pandemic could result in an outbreak. “If we get a positive signal for polio, we’ll look at that specific drainage area, and perhaps do increased vaccinations or community education,” Berkowitz says.Remote areasThe advantage of wastewater testing is that it samples the population without requiring any action from individuals. It’s also cost-effective, because a single sample can be tested for multiple pathogens. And most labs can do the testing. “If you have a medical set-up for pathogen testing,” says Janet Mans, a virologist at the University of Pretoria, “you should be able to do this.”But the technology also has downsides. It’s not easy to tie a signal to a specific location or group, for instance. Furthermore, it monitors only households that are connected to the sewerage system. And some 40% of the nation’s households do not have a flush toilet attached to the sewers, according to the 2011 census. Extending testing to these areas would capture much more of the population.

A centrifuge is used to concentrate the samples.Credit: Delwyn Verasamy/Mail & Guardian

In Pretoria, a private facility known as Waterlab is working with the WRC to expand testing to unsewered communities. The idea is to build a framework to start using samples from rivers and surface waters, says Gina Pocock, Waterlab’s specialist consultant.To monitor trends over time, samples are taken from sites that are consistently contaminated with waste water. That includes rivers downstream of unsewered, informal (or unplanned) settlements and surface run-off both of ‘grey’ water from bathing and sinks around standpipes and of ‘black’ water, which pools near latrines and contains faecal matter.Such samples can be difficult to work with. Mans, who is working with Waterlab on ways to extract and test nucleic acids, says that environmental samples often contain compounds that can inhibit the amplification of nucleic acid by PCR, so addition of internal control sequences are a must. If the internal control is still inhibited after the sample has been diluted by one part to ten, that sample is considered invalid, says Mans. A target can be considered negative only if the internal control amplifies at that dilution.Equally difficult is the analysis, especially determining how many people the sample might represent. The researchers have to use overall trends in other parameters as proxies for the number of people. At Waterlab, Pocock says, researchers use bacterial density “to get an indication of the faecal load in the water”.We don’t know “how many people flushed their toilet this morning”, Pocock says. “It’s not a definite science, where you can say this is our viral load and X amount of people in this community are sick. And with the rivers, even less so. So, we look at trends.”Pros and consThose trends should help in assessing the effectiveness of South Africa’s response to COVID-19.Rolf Halden, director of the Biodesign Center for Environmental Health Engineering at Arizona State University in Tempe, has been testing waste water for nearly 20 years. Last year, he took part in a study to look at the feasibility of mass surveillance, testing the waste water of 36 million people in 100 US cities twice a week for 8 weeks for SARS-CoV-2. His goal is to scale that up to one billion people globally. He and his team found that it was possible to collect a lot of actionable information for very little investment, while still protecting people’s privacy.
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Although obviously enthusiastic about the technology’s potential, Halden acknowledges its shortcomings. For instance, the temperature at the monitoring site matters, as does the distance that people live from it. A signal at a monitoring site could come from a single person close to the site, or from 10,000 people farther away, he explains.There are also ethical and moral considerations as the technology becomes more powerful. Aggregated data on populations are generally considered safe from a privacy perspective, because individuals cannot be identified. However, as analytical techniques advance, it might become possible to identify human DNA, prompting concerns about who should have access to both the technology and the data (D. Jacobs et al. IEEE Trans. Technol. Soc. https://doi.org/gc9m; 2021). “The moral and ethical framework has to grow, ideally before the technology is applied,” Halden says.Still, the potential benefits remain powerful motivators. Wastewater testing, Halden says, allows researchers to keep “a finger on the pulse of humanity”. More

Data for estimating drawdown areasThe calculation of drawdown areas was based on monthly time series of surface-area values for 6,818 reservoirs provided by GRSAD18. It comprises all reservoirs from the Global Reservoir and Dam dataset19 except of 45 reservoirs without reported geometric information. In accordance with ref. 1, we further removed 24 reservoirs classified as natural lakes that have been modified with water regulation structures (this includes lakes Victoria, Baikal and Ontario). The GRSAD dataset comprised entries from March 1984 to October 2015. To have a constant number of data points per year, we restricted our analysis to the period from January 1985 to December 2014.GRSAD was created by correcting the Global Surface Water dataset31 for images contaminated with clouds, cloud shadows and terrain shadows. With this correction, the number of effective images that can be used in each time series has been increased by 81% on average. Substantial improvements have been achieved for reservoirs located in regions with frequent cloud cover and high-latitude reservoirs in the Northern Hemisphere, where low illumination has previously resulted in missing area values during winter months.Calculation of drawdown areasWe calculated monthly drawdown areas for all reservoirs contained in GRSAD according to:$${rm{DA}}=left({{rm{Area}}}_{{rm{max }}}-{rm{Area}}right)/{{rm{Area}}}_{{rm{max }}}$$
(1)
where DA is the relative extent of the drawdown area for a given reservoir considering the current monthly surface area (Area) and the maximum area recorded during the period 1985–2015 (Areamax). We assumed that the maximum area of each reservoir recorded during the 30-year period is a valid representation of its nominal surface area (the area of the reservoir at maximum filling level).Complete filling of reservoirs was defined by a drawdown area smaller than 5% of Areamax. Because there is no uniform definition of ‘extreme drawdown’, we used the Cape Town water crisis 2018 as a reference21. The number of reservoirs experiencing extreme drawdown was estimated by averaging the number of reservoirs with drawdown areas exceeding 40%, 50%, 60% or 70% of Areamax at least once. To prevent initial filling of reservoirs being identified as extreme drawdown, 791 reservoirs built during the analysed period (year built ≥ 1985) were excluded from this analysis. The upper bound (70%) corresponds to the drawdown-area extent during the Cape Town water crisis 201821 (Fig. 1a). The lower bound (40%) corresponds to a reservoir capacity (storage water volume) of approximately 35%, as remained available during that water crisis, assuming an idealized, triangular reservoir shape (Extended Data Fig. 8). This was estimated according to:$$0.36=frac{{left(0.6times sqrt{2}right)}^{2}}{2}$$
(2)
For the calculation of total global drawdown area, used for the upscaling of GHG emissions, we combined data for reservoirs larger than 10 km2 with values derived from a Pareto model for smaller reservoirs. First, we estimated total reservoir surface area for nine size classes following a Pareto distribution. Subsequently, we estimated total drawdown area for each size class by multiplying the size-class-specific relative drawdown-area extent by the total reservoir surface area of each size class (Supplementary Table 3). Because the relative drawdown-area extent for reservoirs smaller than 0.001 km² is unknown and furthermore considered as being imprecise for reservoirs smaller than 10 km², we derived estimates for these size classes on the basis of four different statistical models (linear, square root, logarithmic, polynomial; Extended Data Fig. 9). Reservoirs larger than 10 km² were used to fit linear, square root and logarithmic models, whereas all available data were used for fitting a second-degree polynomial model to achieve a best representation of the available data. The four models all have a constant (linear model) or decreasing (square root, logarithmic, polynomial) slope. We have refrained from using models with increasing slopes (for example, exponential) to not overestimate the drawdown extent of small reservoirs and, thus, consider these estimates as conservative.Data analysisStatistical models to predict drawdown-area extent for each reservoir were developed using stepwise MLR. Climatic data (mean annual temperature, precipitation seasonality) for all reservoir locations were extracted from the Climatologies at High Resolution for the Earth’s Land Surface Areas climate dataset, which gives high-resolution (0.5 arcmin) climate information for global land areas over the period 1979–201332. Climate zones in the Köppen–Geiger system were determined from the high-resolution (5 arcmin) global climate map derived from long-term monthly precipitation and temperature time series representative for the period 1986–201033,34. Data on baseline water stress were extracted from Aqueduct 3.025. Baseline water stress measures the ratio of total water withdrawals to available renewable surface and groundwater supplies and is derived from high-resolution (5 arcmin) hydrological model outputs using the PCR-GLOBWB 2 model35,36.Dates were categorized into four seasons on the basis of their meteorological definition depending on hemisphere. Therefore, for the Northern Hemisphere, spring begins on 1 March, summer on 1 June, autumn on 1 September and winter on 1 December. For the Southern Hemisphere, spring begins on 1 September, summer on 1 December, autumn on 1 March and winter on 1 June.For the analyses of reservoir use types, we used the information provided in the column ‘MAIN_USE’ of the Global Reservoir and Dam dataset. Reservoirs where the main use was not specified (n = 1,554) were combined with those having MAIN_USE = ‘Other’ (n = 205).To identify the magnitude of trends in time series, we used the non-parametric Theil–Sen estimator and the Mann–Kendall test because they do not require prior assumptions of statistical distribution for the data and are resistant to outliers. The Theil–Sen estimator was used to compute the linear rate of change, and the Mann–Kendall test was used to determine the level of significance. We analysed differences between groups using the Kruskal–Wallis test and Dunn’s post hoc test. The threshold to assess statistical significance was 0.05 for all analyses, The statistical analyses were performed using R 3.4.437.Upscaling of GHG emissions and OC burialBecause the global reservoir area derived in this study differed from the area used in previous studies, we recalculated the published global estimates for both OC burial6 in and GHG emissions1 from reservoirs to allow for comparison (Extended Data Fig. 10). We fitted empirical distributions to CO2 emission data from drawdown areas (Supplementary Table 2 and Extended Data Fig. 7) as well as the published OC burial rates6 and published GHG emission data1 from water surfaces of reservoirs. For CO2 emissions from drawdown areas, we used a gamma distribution to account for non-normality of the data (Extended Data Fig. 7). For CO2 and N2O emissions from the water surface, we fitted a skewed normal distribution because of the occurrence of negative values (Extended Data Fig. 7). For CH4 emissions from the water surface, we fitted a log-normal distribution (Extended Data Fig. 7). Because the global estimate of OC burial was derived using geostatistical modelling, we fitted a gamma distribution to the published moments of OC burial rate6 (mean ± s.d. = 144 ± 75.83 gC m−2 yr−1) where the s.d. is calculated as the s.d. of the four scenarios used in that study. The final global empirical distributions for all fluxes were estimated by multiplying average emission and burial rates derived from resampling the preceding distributions times the total water surface area and drawdown area of reservoirs, resulting also from resampling their distributions after uncertainty propagation (see Treatment of uncertainty).Treatment of uncertaintyAs in all upscaling exercises, the global analysis conducted in this study is subject to substantial uncertainty. In our case, the uncertainty results from both the quantification of water surface and drawdown area of reservoirs and the estimation of global rates for GHG emission and OC burial. To comprehensively take all sources of uncertainty into account, we propagated all uncertainty throughout the whole analysis using a combination of Taylor series expansion and Monte Carlo simulations (Extended Data Fig. 10). In brief, we applied customary equations for uncertainty propagation derived from the Taylor series expansion method when propagating uncertainty of moments (for example, mean) or simple arithmetic calculations (for example, multiplication). For more-complex situations or when non-normality was conspicuous, we used Monte Carlo propagation. To obtain global estimates and standard error of water surface and drawdown area of reservoirs, both the systematic (bias) and random uncertainties of the remote-sensing-derived dataset18 as well as the uncertainty induced by our Pareto modelling for reservoirs More