Markus Andrews

Overview of paper search outcomeA general overview of the 231 general water consumption-related and 48 framework analysis scientific publications reviewed in this study (Fig. 3) shows that the number of papers published per year from the general water consumption-related set has been increasing, particularly since the early 2000s. Peaks of more than 10 papers per year in this category emerge since 2011, with a maximum peak of 34 papers recorded in 2018. This increasing trend in time can be attributed to the increasing development of smart metering studies, which have been increasingly allowing detailed household water demand/consumption and behavioral analysis20,47. As a selected subset of the general water consumption-related papers set, the number of framework analysis papers has also increased in the last decade, compared to the ’80s and ’90s, constituting up to 5 papers per year. The final set of papers includes small-case studies comprising only a few units (11 individual households are considered as a minimum in48), as well as large-scale studies comprising several thousands of households (e.g., more than 8000 individual households are considered in49), or entire communities/towns50.Fig. 3: Temporal development of the literature on the determinants of household water consumption.The yearly count of the 231 general water consumption-related (blue) and 48 framework analysis (orange) scientific publications reviewed in this study is represented for the last forty years.Full size imageFigure 4 shows the locations of the studies from the framework analysis set, with larger blue dots indicating more studies. The geographical distribution of the reviewed studies indicates that the interest in the determinants of water use is worldwide. Prominent interest emerges in particular areas, such as the US west coast, the east coast of Australia, and the Mediterranean area in Europe, perhaps reflecting the combination of areas more prone to drought and/or having the higher economic capacity to undertake water use related research.Fig. 4: Geographical locations of the 48 framework analysis papers.The location of the 48 framework analysis papers reviewed in this study is represented with blue markers. Marker size is proportional to the amount of studies in a specific location.Full size imageDeterminant representation by classFrom the analysis of the 48 framework analysis papers, we identified a range of heterogeneous determinants and quantified different combinations of determinant classes, namely observable, latent, and external (see Determinant classification). Figure 5 shows an overview of the representation for the different classes of determinants over the 48 analyzed papers. Observable determinants were the most popular (47 total studies, i.e., 98% representation), with latent and external having lower representation of 52% and 56%, respectively. The values represented in the figure confirm our hypotheses that observable determinants are more common in literature than latent determinants, due to their availability in public databases, either at the household level or at coarser spatial sub-urban scales (e.g., census data collected at the block group-level, such as those used in49). The slightly higher representation of external determinants, compared to latent determinants, is also as expected due to the widespread availability of weather records (e.g., temperature, rainfall) from national or international environmental agencies. While there is no full consensus in the literature on the effect of weather or price variables on water consumption51,52,53, the high degree of representation of external determinants demonstrates that they are considered in more than half of the studies.Fig. 5: Venn diagram of household water consumption determinants representation.The representation of different classes of determinants (observable, latent, and external) in the 48 reviewed framework analysis papers is represented with coloured circles. Intersections are also visualized. The size of each circle and the numerical labels indicate the number of studies in which each combination of determinant classes appeared.Full size imageIt is worth observing that multiple classes of determinants are simultaneously analyzed in most of the reviewed studies, with fewer than 20% analyzing observable variables alone. Further, almost every time external or latent variables are considered, they appear in combination with observable variables. Only one study specifically focused on analyzing the motivations for using and conserving water based on only latent determinants42, while no studies exclusively considered external variables. In contrast, nearly 30% of the studies included both observable and external variables, approximately 23% of the studies considered latent and external variables simultaneously, and 27% of the studies included all three types of determinant classes.The high representation of observable determinants (Fig. 5) suggests that observable variables are widespread in the literature on modelling and forecasting of household water consumption. The prevalence of this class of determinants seems also to confirm the findings from previous studies, which demonstrated that meteorological variables have a greater influence on medium-term prediction and urban/suburban scales, but socio-demographics become more relevant when household-scale and short-term water demand models are developed54,55.Individual determinant representation, impact, and effortTo facilitate interpretation of the numerical values we obtained for the three determinant assessment criteria (i.e., representation, impact, and effort) we defined some regions of interest for each criterion based on thresholds (see the regions labelled as low/high/very high in Fig. 6). We selected the threshold values used to delimit the above regions of interest based on visual inspection of the empirical distribution of the representation, impact, and effort values. This simplification is carried out to facilitate the inference of general qualitative conclusions, while accounting for the low number of papers and, at the same time, high number of determinants. As a result, representation values above/below 30% are considered high/low. Impact values below 75% are considered low, values between 75% and 90% are considered high, and values above 90% are considered very high. Effort rate values above/below 8 are considered high/low.Fig. 6: Individual analysis of representation, impact, and effort.The three criteria to perform determinant analysis, i.e., representation (top), impact (middle), and effort rate (bottom) are associated with individual determinants. Observable determinant class is shown in green, latent class in blue, and external in orange. Shaded background indicates different levels of intensity for each analysis criterion. See Tables 1–3 for determinant acronyms definition (the determinants included in the categories marked as “Other” in the tables are not represented for better clarity).Full size imageFrom the resulting data visualized in Fig. 6, we can infer the following insights about determinant representation, impact, and effort. First, the determinants with the highest representation (top plot in Fig. 6) were household income ( > 70%), family size ( > 60%), and age ( > 45%). As already suggested by the outcomes of class representation (Fig. 5), all the above determinants with high representation are observable. One exception is the awareness determinant, which is the only non-observable determinant we found with high representation. The majority of the other determinants had a representation rate of 10% to 30%.Second, the number of determinants with a high or very high impact (middle plot in Fig. 6) is much larger than the number of determinants with high representation. It must be noted that a high impact does not necessarily mean that a determinant was found to have a high influence on water consumption, but rather that it was found to have some influence on water consumption in many publications. Interestingly, some determinants from all classes achieve high or very high levels of impact. Observable determinants with very high impact include socio-demographic information (number of occupants), house characteristics (house age, value), and outdoor characteristics (garden size, and presence of rainwater tanks). While these latter attributes related to gardens ranked among those with the highest impact, garden composition was found to have one of the lowest impact rates across the analyzed studies. Also, the observable determinants related to the education level of occupants was found to have low impact. A latent variable that emerges as very important (GARD_C) is also related to garden characteristics, but, rather than representing any physical variable, it accounts for the psychological value given by occupants’ attitudes and habits towards gardening. Finally, all external variables were found to have high or very high impact, with rainfall and water price emerging as the two with impact above 90%.The bottom plot of Fig. 6 shows that there was a wide variability in the effort rate for each individual determinant. Data on most of the observable determinants can be generally gathered with low effort, but some (e.g., appliance inventory and irrigation system) require house visits, and thus require high effort. In turn, all latent variables display an effort rate higher than 6, and three out of four are classified as high-effort. Conversely, data on all external determinants can be retrieved with low effort, as they are usually available from national agencies (weather data) and water utilities (water price). Obtaining information on higher effort determinants likely requires getting in contact with individual householders, via phone/online surveys, or house visits.Overall, the results reported in Fig. 6 suggest that there are trade-offs between representation, impact, and effort. In the next section, we perform a joint analysis of the three criteria and their trade-off to infer the implications of the outcomes of this study for researchers and practitioners.Trade-off analysis and implications for researchers and practitionersFigure 7 shows the interaction between the representation, impact, and effort criteria. The distribution of blue and orange points in the figure demonstrates that there are different trade-offs among the three criteria. Each trade-off can have a different set of implications to derive recommendations for researchers and also practitioners. We identified the three groups of determinants marked with (A), (B), and (C) to illustrate the different needs of research and practice. Group A is characterized by high impact, high representation, and low effort. Determinants in this group include household family size, occupants’ age, and occupants’ income. This group of well-studied determinants with proven impact might be particularly interesting for practitioners aiming to gather knowledge on household water consumption with budget constraints. Group (B), which includes, among others, information on the household irrigation system, appliance efficiency, and occupant gender, is characterized by medium-to-high impact, but low representation, and a range of low to high effort. While this group might not be very appealing for practitioners due to low representation, researchers might be interested in focusing on these determinants to increase their representation and, thus, validate or contrast the limited findings on these determinants that appear in the literature. Finally, Group C refers to determinants with low representation and, compared to those in groups A and B, lower impact. As they also might require high gathering efforts, these determinants should be treated with caution until more research is performed to prove their potential impact on a larger sample of studies.Fig. 7: Trade-off analysis of determinant representation, impact, and effort.Impact (x-axis) vs Representation (y-axis) vs Effort (color) of each determinant. Each point refers to a specific determinant. See Tables 1–3 for determinant acronyms definition. The determinants classified as “High effort” are those with an effort value larger than 8.0, vice-versa for the “Low effort” determinants. Determinants are organized in three groups: Group A – high impact, high representation, and low effort; Group B – medium-to-high impact, low representation, and mixed low and high effort; Group C – low representation, low impact, mixed effort.Full size imageAccounting for similar trade-offs across the entire sample of determinants that we have identified from the review of the literature enables determinant-specific recommendations to be derived for practitioners and researchers. In the last step of this review and determinant classification effort we thus develop a trade-off analysis framework that considers different combinations of representation, effort, and impact to formulate such recommendations. In keeping with the goal of this study, our trade-off analysis aims at identifying groups of determinants that have proven cost-effective impact via extensive research and, thus, can be recommended for use in practice, compared with groups of determinants that require more research to address open questions related to representation, impact, and effort. The proposed trade-off analysis framework includes four main recommendation categories:UIn this category, we include determinants characterized by high representation, high/very high impact, and low effort. We consider these determinants as determinants that practitioners can “definitely use” (U), as they have been extensively researched and have been shown to have an impact in most cases, while being affordable. For the same reasons, higher levels of research priority should be devoted to less explored determinants, while these can serve as references. The determinants included in box (A) in Fig. 7 belong to this group.IR-UCIn this category, we classify those determinants characterized by low representation, high/very high impact, and low effort. Given their promising, but not extensively proven, impact, and overall affordability, further research on these determinants should be prioritized to increase their representation (IR). We consider these determinants as determinants that practitioners can “use with caution” (UC), as they have not been extensively researched, but at the same time might have high impact at low-cost. The determinants included in box (B) in Fig. 7 and classified as low effort (blue color) belong to this group.LE/IR-UCIn this category, we include determinants characterized by generally low representation, high/very high impact, and high effort. Similarly to the previous category, we believe that practitioners can use these determinants “with caution” (UC), as they have not been extensively researched and require high effort for data collection, but at the same time might have high/very high impact. Given their promising, but not extensively proven, impact, and high cost, further research on these determinants should be prioritized, primarily to lower the effort (LE) needed to collect them and, thus, facilitate their consideration in more studies (increased representation – IR). The determinants included in box (B) in Fig. 7 and classified as high effort (orange color) belong to this group.IR/LE/AI-NPIn this category, we include determinants characterized by low representation, low impact, and mainly high effort. Given the limited knowledge on these determinants, we suggest that these determinants are “not prioritized” (NP) for use by practitioners unless further research demonstrates that the effort required to collect these determinants is worth the benefit of considering them. Further research should then aim at increasing their representation (IR), lowering the effort needed to obtain data on these determinants (LE), and further assessing their impact (AI) to acquire better knowledge on their actual value. The determinants included in box (C) in Fig. 7 belong to this group.Summary information on the above categories is reported in Table 5. Based on the proposed trade-off analysis framework and the threshold values defined in Fig. 6, we associated each of the different determinants identified in the framework analysis papers with a level of recommendation (see Fig. 8). Some relevant insights for researchers and practitioners emerge. First, only observable determinants are classified as “U”. At present, there are some socio-demographic determinants (i.e., number of occupants, income level, and occupant age) that can be reliably used by practitioners in most cases to model household water consumption and can be easily and affordably retrieved.Table 5 Framework for trade-off analysis, based on representation, impact, and effort.Full size tableFig. 8: Household water consumption determinant classification and associated recommendations for practitioners and researchers based on individual determinants.Each household water consumption determinant identified in the framework analysis papers is associated with a level of recommendation. Determinants are classified according to the three defined classes (columns), i.e., observable, latent, and external. Four levels of recommendation (rows) are formulated for practitioners and researchers. They are sorted in decreasing order of representation and proven impact in research, as well as confidence for use in practical applications. Confidence for use in practical applications decreases going from green (“U” level of recommendation) to orange (“IR/LE/AI-NP” level of recommendation).Full size imageSecond, all external variables (i.e., average rainfall, temperature, and water price) are classified as IR-UC. Consequently, they have a proven impact, but have been used sporadically in connection with household water consumption (while they have been used more often at larger, urban scales), thus results might be case-specific and further research is needed to assess their impact on a larger number of studies.Third, a mix of observable and latent external variables deserves further research to lower effort (e.g., by improving technology/data gathering practices or identifying lower-effort proxies for the same type of information) and increase representation. These variables are either observable determinants, the collection of which requires significant effort and house visits/calls to occupants (e.g., to build an inventory of appliance efficiency or storing information on irrigation systems), or latent variables the impact of which is still not proven due to low representation. The increasing availability of high-resolution metering and behavioral studies fostered by smart metering development is likely to contribute more knowledge on these determinants and more complete guidelines for use by practitioners in the coming years7,17,20.Fourth, we would like to stress that the recommendation “Do not favor adoption until further research” for the determinants classified as IR/LE/AI-NP does not mean that they should not be considered in future applications or no research should be done on them. Conversely, we recognize that many existing studies are based on limited data or data with coarser spatio-temporal resolutions, thus conclusive statements on the impact of such determinants would require further validation. Since large uncertainty about their impact remains, more studies are actually needed to increase the representation of these determinants and increase the statistical significance and generality of their impact assessment. Joint research that also includes other determinants with higher levels of representation could be beneficial to discover more information on the determinants in this group and better understand whether practitioners should eventually include one/more of these determinants in their analysis. Further research could be also developed to assess the degree to which these determinants are correlated with others, and hence redundant, and to which extent these and other determinants can relate to particular characteristics of water consumption (e.g., demand peaks, end use components).Finally, some of the determinants that we recommend to use with caution (UC) in practice, and that should be prioritized for research, might become determinants to definitely use (U) in the future. Two limitations currently prevent us to recommend “definitely use” for these determinants, i.e., generally low representation and high effort for data collection. Low representation indicates that the determinant has not been well-studied in the literature. Hence it might not be generalizable to a wide range of locations. To address the disadvantages of low representation, the following is recommended for practitioners:

Check the literature and if there are studies with similar context (location/climate/application) to the practitioners’ required application, and the impact of the determinant is high, then the determinant could be considered for use.

Continue to monitor the literature, to see if new studies appear using that determinant.

The other limitation, high effort, means that in the reviewed past studies it has been costly for practitioners to collect some of the required determinants. With the advent and widespread use of new technologies, the effort required to collect some of the required high-effort determinants may be substantially reduced. Lowering the effort related to some high-impact, yet also high-effort, determinants (see, e.g., those indicated with orange color in Group B in Fig. 7) would have a two-fold benefit. The direct reduction of the costs required to collect information on those determinants will also enable wider consideration of these determinants in a larger number of studies, thus increasing their representation. As technology enhances the capture of such determinants, there is an opportunity to revisit past studies/datasets and increase the representation of these determinants, which might then transition to determinant group A in in Fig. 7. To address the current limitations and disadvantages of high effort, the following is recommended for practitioners:

Monitor the use of emerging technologies that provide an opportunity to lower the cost required to collect the determinant. For example, there is an opportunity for analysis of high resolution satellite maps/photos to provide automated estimates of observable determinants such as garden size (GSZE) over large number of households, which would lower the cost substantially56. Similarly, latent determinants such as water consumption awareness (AWARE_C) could be based on the uptake of user-friendly smart metering and phone apps on water consumption if they were widely available17,57.

Evaluate overall costs vs benefits based on preliminary experiments on small sample data (to evaluate benefits while avoiding high costs), and consider the use of lower cost proxy data for the “high effort” determinant.

These recommendations provide some guidance for practitioners to handle determinants classified as “use with caution”.Limitations and future researchThis work provides evidence and a quantitative framework for the analysis of household water consumption determinants, yet several limitations and questions remain for further research. First, alternative formulations of determinant representations, impact, and effort could lead to different results. This also stands for the subjective thresholds we adopted to distinguish between high and low representation, impact, and effort. Such thresholds and criteria formulation could be changed based on needs and subjective judgement.Second, in this review we focused on the analysis of individual determinants of household water consumption. However, some determinants could be correlated, present redundant information, or be accounted for in alternative ways to build models for forecasting water demand (e.g., rainfall amount versus rainfall occurrence53). Input feature engineering, variable redundancy, and data accuracy can substantially affect the performance of water demand models. Future studies focused on comparative analysis of alternative determinant formulations and inter-links/dependencies among different determinants can help define non-redundant determinant sets to train models of water demand and recommendations for variable pre-processing.Third, the findings of this study are consistent with previous review papers that identified both observable and latent variables as the most important with respect to domestic water consumption58. Yet, other meta-analyses and review studies found partly contradictory results. Differently from our study26, found that the most important determinants of water use behaviour are related to individual opportunities and motivations, gender, income, and education level. In turn59, formulated a model that accounted for a wide range of variables including demographics, dwelling characteristics, household composition, conservation intention, trust, perceptions, habits, and perceived behavioral control. It must be noted that the above studies do not consider household water consumption per se as we do here, but relate potential determinants of water consumption also to individual consumption or behavior changes (i.e., changes in water consumption over time). Future, potentially contrasting, studies could then expand the scope of this work and relax the exclusion criteria we adopted here to achieve more inclusive comparative analyses that investigate the effect of different determinants in relation to quantified intervals of total household water consumption, and other heterogeneous aspects of domestic water demand, including statistics on end use components (e.g., flow rate, duration, or frequency of individual appliances)60 and temporal changes of water consumption levels due to external stressors such as droughts, or demand management interventions39,49, for example.Fourth, the set of framework analysis papers includes case studies primarily located in the United States, Australia, and Europe (see Fig. 4). Geographical coverage is thus skewed. There is a need for more studies from other geographical regions (including countries with low-income economies) in order to obtain a more balanced picture and consolidate/expand the results obtained so far.Finally, recent works have highlighted that urban and household water demands have been modelled at different spatial and temporal resolutions47. The choice of the temporal and spatial resolution of interest is determined both by data availability and the specific modelling and management purpose. Multi-scale studies combining different levels of spatial and temporal aggregation of water demands and potential determinants would further advance our analysis and contextualize specific recommendations for data collection and processing at the different spatial and temporal scales of interest. More

Chasing Icebergs: How Frozen Freshwater Can Save the Planet Matthew H. Birkhold Viking (2023)The flavour of chilled Svalbarði-brand water, melted from an iceberg just 1,000 kilometres from the North Pole, is described by the company as “like catching snowflakes on the tongue”. Bottled in Longyearbyen, a tiny metropolis in Norway’s Svalbard archipelago, Svalbarði water is airlifted to luxury locales in London, Sydney, Florida and Macau. “Taste the Arctic to Save the Arctic,” its website croons, promoting the supposed carbon neutrality of the water, which sells online for €99.95 (US$107) for a 750-millilitre bottle.That price is far out of reach for most of the world’s people, including the one in four who lack safe drinking water. In Chasing Icebergs, Matthew Birkhold, a scholar of law, culture and the humanities, considers whether it’s possible to slake the world’s thirst with the two-thirds of global fresh water that is locked away in ice caps and glaciers — “stuck at the poles in gigantic fortresses of ice”, in his words.Some are already at it — and not just epicureans quaffing Svalbarði. In Newfoundland, Canada, Birkhold interviews “iceberg cowboy” Ed Kean, who wrangles bergs from the frigid sea, selling the water to cosmetics companies and breweries. In Qaanaaq, Greenland’s northernmost town, Birkhold notes, the public water supply includes filtered and treated iceberg melt.
Towing icebergs to arid regions to reduce water scarcity
There’s more where that came from. By one estimate, some 2,300 cubic kilometres of ice breaks off from Antarctica every year. More than 100,000 Arctic and Antarctic icebergs melt into the ocean annually, according to a 2022 United Nations report. A relatively small, 113-million-tonne iceberg, says Birkhold, could be towed from Antarctica to Cape Town, South Africa, to supply 20% of the city’s water needs for a year. What’s not to like?Quite a lot, perhaps. “To write this book, I have talked to dozens of scientists,” Birkhold writes. “They are uniformly dubious.” Palaeoclimatologist Ellen Mosley-Thompson has led nine expeditions to Antarctica and six to Greenland to extract ice cores. “Make sure you write that I am skeptical of iceberg towing,” she instructs him.Less sceptical is master mariner Nick Sloane, the brains behind the Cape Town plan. Using satellite data to find the best berg, his “team of glaciologists, engineers and oceanographers” plans to catch it in a giant net and pull it by tugboat into the mighty Antarctic Circumpolar Current, and thence into the north-flowing Benguela Current towards South Africa.

Icebergs are sometimes towed to prevent damage to oil platforms.Credit: Greg Locke/Reuters

Sloane has estimated the cost at a cool $100 million, plus an extra $50 million or so to melt the ice and funnel the fresh water to land, if the iceberg hasn’t melted into the sea or fallen apart en route. In various interviews, Cape Town officials have expressed themselves as less than enthused.Sloane’s plans have yet to materialize. Meanwhile, POLEWATER, a Berlin-based company, has been working for almost a decade on a similar plan — to haul frozen fresh water to the western coast of Africa and the Caribbean, where the company intends to give it away to those in dire need. It, too, will use satellites to locate suitable bergs, but once it has relocated them, it plans to pump meltwater from pools on top into easily transportable gigantic bags.Then there’s the UAE Iceberg Project, the dream of Emirati inventor Abdulla Alshehhi to import an Antarctic iceberg to the Fujairah coast of the United Arab Emirates. An animated promotion features penguins and polar bears — species hailing from opposite poles — posing dolefully on the iceberg. Alshehhi has said that “it will be cheaper to bring in these icebergs” than to desalinate seawater — a common technique in the Middle East.
World’s largest ice sheet threatened by warm water surge
Desalination provides at least 35 trillion litres of drinking water globally every year. Birkhold notes that it is prohibitively expensive in many places. It also relies on fossil fuels for energy, and pollutes the ocean with excess salt. But he offers limited information on other, perhaps more effective, alternatives to iceberg towing, such as recycling municipal wastewater or tapping brackish water for crop irrigation. He offers no data on more esoteric sources of water, such as fog harvesting, used by remote communities in Chile, Morocco and South Africa. Nor does he address initiatives to reduce water waste or increase efficiency of use.Birkhold speculates that if iceberg harvesting succeeds, it might not remain the province of quixotic entrepreneurs — bigger, water-hungry enterprises might muscle in with their “deep coffers and profit-driven approach”. The race is largely unregulated: few national laws address iceberg use, and no international agreements clarify who can capture and sell these freshwater resources. If they are to be exploited fairly, the author concludes, “we need to decide who gets to use icebergs — and how and how many — in a way that is just and equitable”.Birkhold is engagingly honest about potential pitfalls. But if the enterprise could succeed, enormous quantities of fresh water that would otherwise melt into the ocean could be delivered to parched regions. Kudos to the author for diving in — whether or not it is ever realized.

Competing Interests
The author declares no competing interests. More

Afolalu, S. A., Ikumapayi, O. M., Ogedengbe, T. S., Kazeem, R. A. & Ogundipe, A. T. Waste pollution, wastewater and effluent treatment methods – an overview. Mater. Today Proc. 62, 3282–3288 (2022).Article 
CAS 

Google Scholar 
Jamwal, P. & Shirin, S. Impact of microbial activity on the performance of planted and unplanted wetland at laboratory scale. Water Pract. Technol. 16, 472–489 (2021).Article 

Google Scholar 
Wang, J. et al. A review on microorganisms in constructed wetlands for typical pollutant removal: species, function, and diversity. Front. Microbiol. 13, 845725 (2022).Article 

Google Scholar 
Shruthi, R. & Shivashankara, G. P. Effect of HRT and seasons on the performance of pilot-scale horizontal subsurface flow constructed wetland to treat rural wastewater. Water Pract. Technol. 17, 445–455 (2022).Article 

Google Scholar 
Arden, S. & Ma, X. Constructed wetlands for greywater recycle and reuse: a review. Sci. Total Environ. 630, 587–599 (2018).Article 
CAS 

Google Scholar 
Ali, M., Rousseau, D. P. L. & Ahmed, S. A full-scale comparison of two hybrid constructed wetlands treating domestic wastewater in Pakistan. J. Environ. Manag. 210, 349–358 (2018).Article 
CAS 

Google Scholar 
Corbella, C. & Puigagut, J. Improving domestic wastewater treatment efficiency with constructed wetland microbial fuel cells: Influence of anode material and external resistance. Sci. Total Environ. 631–632, 1406–1414 (2018).Article 

Google Scholar 
Rajan, R. J., Sudarsan, J. S. & Nithiyanantham, S. Microbial population dynamics in constructed wetlands: Review of recent advancements for wastewater treatment. Environ. Eng. Res. 24, 181–190 (2019).Article 

Google Scholar 
Karajić, M. et al. Microbial activity in a pilot-scale, subsurface flow, sand-gravel constructed wetland inoculated with halotolerant microorganisms. Afr. J. Biotechnol. 11, 15020–15029 (2012).
Google Scholar 
Lee, C., Fletcher, T. D. & Sun, G. Nitrogen removal in constructed wetland systems. Eng. Life Sci. 9, 11–22 (2009).Article 
CAS 

Google Scholar 
Hijosa-Valsero, M. et al. Removal of antibiotics from urban wastewater by constructed wetland optimization. Chemosphere 83, 713–719 (2011).Article 
CAS 

Google Scholar 
Takavakoglou, V., Pana, E. & Skalkos, D. Constructed Wetlands as nature-based solutions in the post-COVID agri-food supply chain: challenges and opportunities. Sustain 14, 3145 (2022).Article 
CAS 

Google Scholar 
Si, Z. et al. Mechanism and performance of trace metal removal by continuous-flow constructed wetlands coupled with a micro-electric field. Water Res. 164, 114937 (2019).Article 
CAS 

Google Scholar 
Syranidou, E. et al. Responses of the endophytic bacterial communities of juncus acutus to pollution with metals, emerging organic pollutants and to bioaugmentation with indigenous strains. Front. Plant Sci. 9, 1526 (2018).Article 

Google Scholar 
Vassallo, A. et al. Temporal evolution of bacterial endophytes associated to the roots of phragmites australis exploited in phytodepuration of wastewater. Front. Microbiol. 11, 1652 (2020).Article 

Google Scholar 
Mukherjee, K. & Pal, S. Hydrological and landscape dynamics of floodplain wetlands of the Diara region, Eastern India. Ecol. Indic. 121, 106961 (2021).Article 

Google Scholar 
Bera, T. et al. Pollution assessment and mapping of potentially toxic elements (PTE) distribution in urban wastewater fed natural wetland, Kolkata, India. Environ. Sci. Pollut. Res. 29, 67801–67820 (2022).Article 
CAS 

Google Scholar 
Polz, M. F. & Cordero, O. X. Bacterial evolution: genomics of metabolic trade-offs. Nat. Microbiol. 1, 16181 (2016).Article 
CAS 

Google Scholar 
Dai, T. et al. Nutrient supply controls the linkage between species abundance and ecological interactions in marine bacterial communities. Nat. Commun. 13, 175 (2022).Article 
CAS 

Google Scholar 
Gandhi, S. R., Korolev, K. S. & Gore, J. Cooperation mitigates diversity loss in a spatially expanding microbial population. Proc. Natl Acad. Sci. USA 116, 23582–23587 (2019).Article 
CAS 

Google Scholar 
Jin, D. et al. Bacterial communities and potential waterborne pathogens within the typical urban surface waters. Sci. Rep. 8, 1–9 (2018).Article 

Google Scholar 
Furman, O. et al. Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics. Nat. Commun. 11, 1–13 (2020).Article 

Google Scholar 
Lew, S., Glińska-Lewczuk, K. & Lew, M. The effects of environmental parameters on the microbial activity in peat-bog lakes. PLoS ONE 14, 1–18 (2019).Article 

Google Scholar 
Zheng, F. et al. Comparison and interpretation of freshwater bacterial structure and interactions with organic to nutrient imbalances in restored wetlands. Front. Microbiol. 13, 946537 (2022).Article 

Google Scholar 
Tardy, V. et al. Stability of soil microbial structure and activity depends on microbial diversity. Environ. Microbiol. Rep. 6, 173–183 (2014).Article 
CAS 

Google Scholar 
Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: Networks, competition, and stability. Science 350, 663–666 (2015).Article 
CAS 

Google Scholar 
Elder, F. C. T. et al. Stereoselective metabolism of chloramphenicol by bacteria isolated from wastewater, and the importance of stereochemistry in environmental risk assessments for antibiotics. Water Res. 217, 118415 (2022).Article 
CAS 

Google Scholar 
Lopeman, R. C., Harrison, J., Desai, M. & Cox, J. A. G. Mycobacterium abscessus: environmental bacterium turned clinical nightmare. Microorganisms 7, 90 (2019).Article 
CAS 

Google Scholar 
Zhao, J. et al. Production, purification and biochemical characterisation of a novel lipase from a newly identified lipolytic bacterium Staphylococcus caprae NCU S6. J. Enzyme Inhib. Med. Chem. 36, 248–256 (2021).Article 

Google Scholar 
Syed, A. et al. Heavy metals induced modulations in growth, physiology, cellular viability, and biofilm formation of an identified bacterial isolate. ACS Omega 6, 25076–25088 (2021).Article 
CAS 

Google Scholar 
Delgado-Blas, J. F. et al. Population genomics and antimicrobial resistance dynamics of Escherichia coli in wastewater and river environments. Commun. Biol. 4, 1–13 (2021).Article 

Google Scholar 
Kemper, K., De Goeje, P. L. & Peeper, D. S. Phenotype switching: tumor cell plasticity as a resistance mechanism and target for therapy. Cancer Res. 74, 5937–5942 (2014).Article 
CAS 

Google Scholar 
NCCLS. Performance standards for antimicrobial disk and dilution susceptibility tests for bacteria isolated from animals. in Approved standard-second edition NCCLS document M31-A3 (ed. Wayne, P.) vol. 28 (National Committee for Clinical Laboratory Standards, 2002).CLSI. Performance standards for antimicrobial susceptibility testing; Twenty-Fifth informational supplement. in CLSI document M100-S25 (ed. Wayne, P.) (Clinical and Laboratory Standards Institute, 2015).Davies, J. & Dorothy, D. Origins and evolution of antibiotic resistance. Microbiol. Mol. Biol. Rev. 74, 417–433 (2010).Article 
CAS 

Google Scholar 
Davies, J. Inactivation of antibiotics and the dissemination of resistance genes. Science 264, 375–382 (1994).Article 
CAS 

Google Scholar 
Li, D. et al. Antibiotic resistance characteristics of environmental bacteria from an oxytetracycline production wastewater treatment plant and the receiving river. Appl. Environ. Microbiol. 76, 3444–3451 (2010).Article 
CAS 

Google Scholar 
Rizzo, L. et al. Urban wastewater treatment plants as hotspots for antibiotic resistant bacteria and genes spread into the environment: a review. Sci. Total Environ. 447, 345–360 (2013).Article 
CAS 

Google Scholar 
Balcazar, J. L. Bacteriophages as vehicles for antibiotic resistance genes in the environment. PLoS Pathog. 10, 1–4 (2014).Article 

Google Scholar 
von Wintersdorff, C. J. H. et al. Dissemination of antimicrobial resistance in microbial ecosystems through horizontal gene transfer. Front. Microbiol. 7, 1–10 (2016).
Google Scholar 
Al-Sarawi, H. A., Najem, A. B., Lyons, B. P., Uddin, S. & Al-Sarawi, M. A. Antimicrobial resistance in Escherichia coli isolated from marine sediment samples from Kuwait Bay. Sustain 14, 1–11 (2022).
Google Scholar 
Tejedor-Junco, M. T., Díaz, V. C., González-Martín, M. & Tuya, F. Presence of microplastics and antimicrobial-resistant bacteria in sea cucumbers under different anthropogenic influences in Gran Canaria (Canary Islands, Spain). Mar. Biol. Res. 17, 537–544 (2021).Article 

Google Scholar 
Garcias, B. et al. Extended-spectrum β-lactam resistant klebsiella pneumoniae and escherichia coli in wild European hedgehogs (Erinaceus europeus) living in populated areas. Animals 11, 2837 (2021).Article 

Google Scholar 
Gessew, G. T., Desta, A. F. & Adamu, E. High burden of multidrug resistant bacteria detected in Little Akaki River. Comp. Immunol. Microbiol. Infect. Dis. 80, 101723 (2022).Article 
CAS 

Google Scholar 
Lood, R., Ertürk, G. & Mattiasson, B. Revisiting antibiotic resistance spreading in wastewater treatment plants – Bacteriophages as a much neglected potential transmission vehicle. Front. Microbiol. 8, 1–7 (2017).Article 

Google Scholar 
Pedros-Alio, C. The rare bacterial biosphere. Ann. Rev. Mar. Sci. 4, 449–466 (2012).Article 

Google Scholar 
Lynch, M. D. J. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229 (2015).Article 
CAS 

Google Scholar 
Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).Article 

Google Scholar 
Laland, K., Matthews, B. & Feldman, M. W. An introduction to niche construction theory. Evol. Ecol. 30, 191–202 (2016).Article 

Google Scholar 
Ghoul, M. & Mitri, S. The ecology and evolution of microbial competition. Trends Microbiol. 24, 833–845 (2016).Article 
CAS 

Google Scholar 
Calatayud, J. et al. Positive associations among rare species and their persistence in ecological assemblages. Nat. Ecol. Evol. 4, 40–45 (2020).Article 

Google Scholar 
Goebel, W., Chakraborty, T. & Kreft, J. Bacterial hemolysins as virulence factors. Antonie Van Leeuwenhoek 54, 453–463 (1988).Article 
CAS 

Google Scholar 
Pandey, A., Naik, M. & Dubey, S. K. Hemolysin, protease, and EPS producing pathogenic Aeromonas hydrophila Strain An4 shows antibacterial activity against marine bacterial fish pathogens. J. Mar. Biol. 2010, 563205 (2010).Article 

Google Scholar 
Wang, Z. et al. Plastisphere enrich antibiotic resistance genes and potential pathogenic bacteria in sewage with pharmaceuticals. Sci. Total Environ. 768, 144663 (2021).Article 
CAS 

Google Scholar 
Wang, J. et al. Treatment of hospital wastewater by electron beam technology: removal of COD, pathogenic bacteria and viruses. Chemosphere 308, 136265 (2022).Article 
CAS 

Google Scholar 
Cavalini, L., Jankoski, P., Correa, A. P. F., Brandelli, A. & Da Motta, A. S. Characterization of the antimicrobial activity produced by Bacillus sp. Isolated from wetland sediment. An. Acad. Bras. Cienc. 93, 1–13 (2021).Article 

Google Scholar 
Jankoski, P. R., Correa, A. P. F., Brandelli, A. & Da Motta, A. S. Biological activity of bacteria isolated from wetland sediments collected from a conservation unit in the southern region of Brazil. An. Acad. Bras. Cienc. 93, 1–15 (2021).Article 

Google Scholar 
Seruga, P. et al. Removal of ammonia from the municipal waste treatment effuents using natural minerals. Molecules 24, 3633 (2019).Article 
CAS 

Google Scholar 
Du, Q., Liu, S., Cao, Z. & Wang, Y. Ammonia removal from aqueous solution using natural Chinese clinoptilolite. Sep. Purif. Technol. 44, 229–234 (2005).Article 
CAS 

Google Scholar 
Tamura, K., Stecher, G. & Kumar, S. MEGA11: molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38, 3022–3027 (2021).Article 
CAS 

Google Scholar 
Saitou, N. & Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).CAS 

Google Scholar 
Tamura, K., Nei, M. & Kumar, S. Prospects for inferring very large phylogenies by using the neighbor-joining method. Proc. Natl Acad. Sci. USA 101, 11030–11035 (2004).Article 
CAS 

Google Scholar 
Batoni, G., Maisetta, G. & Esin, S. Antimicrobial peptides and their interaction with biofilms of medically relevant bacteria. Biochim. Biophys. Acta 1858, 1044–1060 (2016).Article 
CAS 

Google Scholar 
Zidour, M. et al. Isolation and characterization of bacteria colonizing acartia tonsa copepod eggs and displaying antagonist effects against Vibrio anguillarum, Vibrio alginolyticus and other pathogenic strains. Front. Microbiol. 8, 1–13 (2017).Article 

Google Scholar 
Krumperman, P. H. Multiple antibiotic resistance indexing of Escherichia coli to identify high-risk sources of faecal contamination of water. Appl. Environ. Microbiol. 46, 165–170 (1983).Article 
CAS 

Google Scholar 
Paria, P. et al. Molecular characterization and genetic diversity study of Vibrio parahaemolyticus isolated from aquaculture farms in India. Aquaculture 509, 104–111 (2019).Article 
CAS 

Google Scholar 
Zheng, X. et al. Essential oils improve the survival of gnotobiotic brine shrimp (Artemia franciscana) challenged with Vibrio campbellii. Front. Immunol. 12, 693932 (2021).Article 
CAS 

Google Scholar 
Taylor, S. M., He, Y., Zhao, B. & Huang, J. Heterotrophic ammonium removal characteristics of an aerobic heterotrophic nitrifying-denitrifying bacterium, Providencia rettgeri YL. J. Environ. Sci. 21, 1336–1341 (2009).Article 
CAS 

Google Scholar 
Pereira, E. L., Borges, A. C. & da Silva, G. J. Effect of the Progressive Increase of Organic Loading Rate in an Anaerobic Sequencing Batch Reactor for Biodiesel Wastewater Treatment. Water 14, 223 (2022).Article 
CAS 

Google Scholar 
Benítez-Chao, D. F., León-Buitimea, A., Lerma-Escalera, J. A. & Morones-Ramírez, J. R. Bacteriocins: An overview of antimicrobial, toxicity, and biosafety assessment by in vivo models. Front. Microbiol 12, 630695 (2021).Simons, A., Alhanout, K. & Duval, R. E. Bacteriocins, antimicrobial peptides from bacterial origin: overview of their biology and their impact against multidrug-resistant bacteria. Microorganisms 8, 639 (2020).Garcia-Garcera, M. & Rocha, E. PC. Community diversity and habitat structure shape the repertoire of extracellular proteins in bacteria. Nat. Commun. 11, 758 (2020).Soler, P., Moreno-Mesonero, L., Zornoza, A., V. Javier Macián, & Moreno, Y. Characterization of eukaryotic microbiome and associated bacteria communities in a drinking water treatment plant. Sci. Total Environ. 797, 149070 (2021).Karri, R. R., Sahu, J. N. & Chimmiri, V. Critical review of abatement of ammonia from wastewater. J. Mol. Liq. 261, 21–31 (2018).Article 
CAS 

Google Scholar 
Royan, M. R., Solim, M. H., & Santanumurti, M. B. (2019, February). Ammonia-eliminating potential of Gracilaria sp. And zeolite: a preliminary study of the efficient ammonia eliminator in aquatic environment. In IOP Conference Series: Earth and Environmental Science (Vol. 236, No. 1, p. 012002). IOP Publishing.Liu, Y., Ngo, H. H., Guo, W., Peng, L., Wang, D. & Ni, B The roles of free ammonia (FA) in biological wastewater treatment processes: A review. Environ. Int. 123, 10–19 (2019).Article 
CAS 

Google Scholar 
Guan, T. W., Lin, Y. J., Ou, M. Y. & Chen, K. B. Isolation and diversity of sediment bacteria in the hypersaline aiding lake, China. PloS one 15, e0236006 (2020).Article 
CAS 

Google Scholar 
Nickum, J. et al. Guidelines for the use of fishes in research. FISHERIES-BETHESDA- 29 3, 26 (2004).Johansen, R., Needham, J.R., Colquhoun, D.J., Poppe, T.T. & Smith, A.J. Guidelines for health and welfare monitoring of fish used in research. Lab. Anim. 40, 323–340 (2006). More

Progress Towards the Sustainable Development Goals. Report of the Secretary-General (United Nations, Economic and Social Council, 2022).Sustainable Development Goal 6 Synthesis Report 2018 on Water and Sanitation (United Nations, 2018).Luoto, J. et al. What point-of-use water treatment products do consumers use? Evidence from a randomized controlled trial among the urban poor in Bangladesh. PLoS ONE 6, e26132 (2011).Article 
CAS 

Google Scholar 
Pickering, A. J. et al. Differences in field effectiveness and adoption between a novel automated chlorination system and household manual chlorination of drinking water in Dhaka, Bangladesh: a randomized controlled trial. PLoS ONE 10, e0118397 (2015).Article 

Google Scholar 
Oteng-Peprah, M., de Vries, N. & Acheampong, M. A. Households’ willingness to adopt greywater treatment technologies in a developing country—exploring a modified theory of planned behaviour (TPB) model including personal norm. J. Environ. Manag. 254, 109807 (2020).Article 
CAS 

Google Scholar 
Tortajada, C. & van Rensburg, P. Drink more recycled wastewater. Nature 577, 26–28 (2020).Article 
CAS 

Google Scholar 
Tortajada, C. & Nam Ong, C. Reused water policies for potable use. Int. J. Water Resour. D 32, 500–502 (2016).Article 

Google Scholar 
Batel, S., Devine-Wright, P. & Tangeland, T. Social acceptance of low carbon energy and associated infrastructures: a critical discussion. Energy Policy 58, 1–5 (2013).Article 

Google Scholar 
Hurlimann, A. & Dolnicar, S. When public opposition defeats alternative water projects—the case of Toowoomba Australia. Water Res. 44, 287–297 (2010).Article 
CAS 

Google Scholar 
Kenney, S. Purifying water: responding to public opposition to the implementation of direct potable reuse in California. UCLA J. Environ. Law Policy 37, 85–122 (2019).Article 

Google Scholar 
Mosler, H.-J. A systematic approach to behavior change interventions for the water and sanitation sector in developing countries: a conceptual model, a review, and a guideline. Int. J. Environ. Health Res. 22, 431–449 (2012).Article 

Google Scholar 
Boisson, S. et al. Effect of household-based drinking water chlorination on diarrhoea among children under five in Orissa, India: a double-blind randomised placebo-controlled trial. PLoS Med. 10, e1001497 (2013).Article 

Google Scholar 
Sonego, I. L., Huber, A. C. & Mosler, H.-J. Does the implementation of hardware need software? A longitudinal study on fluoride-removal filter use in Ethiopia. Environ. Sci. Technol. 47, 12661–12668 (2013).Article 
CAS 

Google Scholar 
Stauber, C. E. et al. A cluster randomized trial of the impact of education through listening (a novel behavior change technique) on household water treatment with chlorine in Vihiga District, Kenya, 2010–2011. Am. J. Trop. Med. 104, 382–390 (2021).Article 
CAS 

Google Scholar 
Hoffmann, S. et al. A research agenda for the future of urban water management: exploring the potential of nongrid, small-grid, and hybrid solutions. Environ. Sci. Technol. 54, 5312–5322 (2020).Article 
CAS 

Google Scholar 
Anthonj, C. et al. Do health risk perceptions motivate water- and health-related behaviour? A systematic literature review. Sci. Total Environ. 819, 152902 (2022).Article 
CAS 

Google Scholar 
Huber, A. C., Tobias, R. & Mosler, H.-J. Evidence-based tailoring of behavior-change campaigns: increasing fluoride-free water consumption in rural Ethiopia with persuasion. Appl. Psychol. Health Well Being 6, 96–118 (2014).Article 

Google Scholar 
Johnston, M. et al. Development of an online tool for linking behavior change techniques and mechanisms of action based on triangulation of findings from literature synthesis and expert consensus. Transl. Behav. Med. 11, 1049–1065 (2021).Article 

Google Scholar 
Belcher, B. M., Davel, R. & Claus, R. A refined method for theory-based evaluation of the societal impacts of research. MethodsX 7, 100788 (2020).Article 

Google Scholar 
Deutsch, L., Belcher, B., Claus, R. & Hoffmann, S. Leading inter- and transdisciplinary research: lessons from applying theories of change to a strategic research program. Environ. Sci. Policy 120, 29–41 (2021).Article 

Google Scholar 
De Buck, E. et al. Approaches to Promote Handwashing and Sanitation Behaviour Change in Low- and Middle-Income Countries: A Mixed Method Systematic Review (Campbell Systematic Reviews, 2017).Inauen, J. et al. Environmental issues are health issues: making a case and setting an agenda for environmental health psychology. Eur. Psychol. 26, 219–229 (2021).Article 

Google Scholar 
Mosler, H.-J. & Contzen, N. Systematic Behavior Change in Water, Sanitation and Hygiene. A Practical Guide Using the RANAS Approach 1.1 edn (Eawag, 2016).Hering, J. G., Waite, T. D., Luthy, R. G., Drewes, J. E. & Sedlak, D. L. A changing framework for urban water systems. Environ. Sci. Technol. 47, 10721–10726 (2013).Article 
CAS 

Google Scholar 
Rabaey, K., Vandekerckhove, T., de Walle, A. V. & Sedlak, D. L. The third route: using extreme decentralization to create resilient urban water systems. Water Res. 185, 116276 (2020).Article 
CAS 

Google Scholar 
Khatri, K., Vairavamoorthy, K. & Porto, M. in Water for a Changing World. Developing Local Knowledge and Capacity (eds Alaerts, G. & Dickinson, N.) 93–112 (CRC Press, 2008).Massoud, M. A., Tarhini, A. & Nasr, J. A. Decentralized approaches to wastewater treatment and management: applicability in developing countries. J. Environ. Manag. 90, 652–659 (2009).Article 

Google Scholar 
Noppers, E. H., Keizer, K., Bolderdijk, J. W. & Steg, L. The adoption of sustainable innovations: driven by symbolic and environmental motives. Glob. Environ. Change 25, 52–62 (2014).Article 

Google Scholar 
Nolan, J. M., Schultz, P. W., Cialdini, R. B., Goldstein, N. J. & Griskevicius, V. Normative social influence is underdetected. Pers. Soc. Psychol. Bull. 34, 913–923 (2008).Article 

Google Scholar 
Huber, A. C. & Mosler, H.-J. Determining behavioral factors for interventions to increase safe water consumption: a cross-sectional field study in rural Ethiopia. Int. J. Environ. Health Res. 23, 96–107 (2013).Article 

Google Scholar 
Chesley, N., Meier, H., Luo, J., Apchemengich, I. & Davies, W. H. Social factors shaping the adoption of lead-filtering point-of-use systems: an observational study of an MTurk sample. J. Water Health 18, 505–521 (2020).Article 

Google Scholar 
Graf, J., Meierhofer, R., Wegelin, M. & Mosler, H.-J. Water disinfection and hygiene behaviour in an urban slum in Kenya: impact on childhood diarrhoea and influence of beliefs. Int. J. Environ. Health Res. 18, 335–355 (2008).Article 

Google Scholar 
Lilje, J. & Mosler, H.-J. Effects of a behavior change campaign on household drinking water disinfection in the Lake Chad Basin using the RANAS approach. Sci. Total Environ. 619–620, 1599–1607 (2018).Article 

Google Scholar 
Murray, A. L. et al. Evaluation of consistent use, barriers to use, and microbiological effectiveness of three prototype household water treatment technologies in Haiti, Kenya, and Nicaragua. Sci. Total Environ. 718, 134685 (2020).Article 
CAS 

Google Scholar 
Kraemer, S. M. & Mosler, H.-J. Persuasion factors influencing the decision to use sustainable household water treatment. Int. J. Environ. Health Res. 20, 61–79 (2010).Article 

Google Scholar 
Heri, S. & Mosler, H.-J. Factors affecting the diffusion of solar water disinfection: a field study in Bolivia. Health Educ. Behav. 35, 541–560 (2008).Article 

Google Scholar 
Daniel, D., Sirait, M. & Pande, S. A hierarchical Bayesian belief network model of household water treatment behaviour in a suburban area: a case study of Palu—Indonesia. PLoS ONE 15, e0241904 (2020).Article 
CAS 

Google Scholar 
Daniel, D. et al. Understanding the effect of socio-economic characteristics and psychosocial factors on household water treatment practices in rural Nepal using Bayesian belief networks. Int. J. Hyg. Environ. Health 222, 847–855 (2019).Article 
CAS 

Google Scholar 
Thaher, R. A., Mahmoud, N., Al-Khatib, I. A. & Hung, Y.-T. Reasons of acceptance and barriers of house onsite greywater treatment and reuse in Palestinian rural areas. Water https://doi.org/10.3390/w12061679 (2020).Gómez-Román, C., Sabucedo, J.-M., Alzate, M. & Medina, B. Environmental concern priming and social acceptance of sustainable technologies: the case of decentralized wastewater treatment systems. Front. Psychol. https://doi.org/10.3389/fpsyg.2021.647406 (2021).Marks, J., Cromar, N., Fallowfield, H. & Oemcke, D. Community experience and perceptions of water reuse. Water Supply 3, 9–16 (2003).Article 
CAS 

Google Scholar 
Domènech, L. & Saurí, D. Socio-technical transitions in water scarcity contexts: public acceptance of greywater reuse technologies in the metropolitan area of Barcelona. Resour. Conserv. Recycl. 55, 53–62 (2010).Article 

Google Scholar 
Portman, M. E., Vdov, O., Schuetze, M., Gilboa, Y. & Friedler, E. Public perceptions and perspectives on alternative sources of water for reuse generated at the household level. J. Water Reuse Desalination https://doi.org/10.2166/wrd.2022.002 (2022).Article 

Google Scholar 
Nancarrow, B. E., Porter, N. B. & Leviston, Z. Predicting community acceptability of alternative urban water supply systems: a decision making model. Urban Water J. 7, 197–210 (2010).Article 

Google Scholar 
Huber, A. C., Bhend, S. & Mosler, H.-J. Determinants of exclusive consumption of fluoride-free water: a cross-sectional household study in rural Ethiopia. J. Public Health 20, 269–278 (2012).Article 

Google Scholar 
MacDonald, M. C. et al. Assessing participant compliance with point-of-use water treatment: an exploratory investigation. Public Work. Manag. Policy 23, 150–167 (2018).Article 

Google Scholar 
Tobias, R. & Berg, M. Sustainable use of arsenic-removing sand filters in vietnam: psychological and social factors. Environ. Sci. Technol. 45, 3260–3267 (2011).Article 
CAS 

Google Scholar 
Contzen, N. & Marks, S. J. Increasing the regular use of safe water kiosk through collective psychological ownership: a mediation analysis. J. Environ. Psychol. 57, 45–52 (2018).Article 

Google Scholar 
Blum, A. G., Null, C. & Hoffmann, V. Marketing household water treatment: willingness to pay results from an experiment in rural Kenya. Water 6, 1873–1886 (2014).Article 

Google Scholar 
Brouwer, R., Job, F. C., van der Kroon, B. & Johnston, R. Comparing willingness to pay for improved drinking-water quality using stated preference methods in rural and urban Kenya. Appl. Health Econ. Health Policy 13, 81–94 (2015).Article 

Google Scholar 
Amaris, G., Dawson, R., Gironás, J., Hess, S. & Ortúzar, J. D. D. Understanding the preferences for different types of urban greywater uses and the impact of qualitative attributes. Water Res. 184, 116007 (2020).Article 
CAS 

Google Scholar 
Nancarrow, B. E., Leviston, Z. & Tucker, D. I. Measuring the predictors of communities’ behavioural decisions for potable reuse of wastewater. Water Sci. Technol. 60, 3199–3209 (2009).Article 
CAS 

Google Scholar 
Po, M., Nancarrow, B. E. & Kaercher, J. D. Literature Review of Factors Influencing Public Perceptions of Water Reuse Vol. 54 (CSIRO Land and Water, 2003).Rozin, P., Haddad, B., Nemeroff, C. & Slovic, P. Psychological aspects of the rejection of recycled water: contamination, purification and disgust. Judgm. Decis. Mak. 10, 50–63 (2015).Article 

Google Scholar 
Wester, J. et al. Psychological and social factors associated with wastewater reuse emotional discomfort. J. Environ. Psychol. 42, 16–23 (2015).Article 

Google Scholar 
Jeffrey, P. & Jefferson, B. Public receptivity regarding ‘in-house’ water recycling: results from a UK survey. Water Supply 3, 109–116 (2003).Article 

Google Scholar 
Brown, R. R. & Davies, P. Understanding community receptivity to water re-use: Ku-ring-gai Council case study. Water Sci. Technol. 55, 283–290 (2007).Article 
CAS 

Google Scholar 
Mankad, A. Decentralised water systems: emotional influences on resource decision making. Environ. Int. 44, 128–140 (2012).Article 

Google Scholar 
Altherr, A.-M., Mosler, H.-J., Tobias, R. & Butera, F. Attitudinal and relational factors predicting the use of solar water disinfection: a field study in Nicaragua. Health Educ. Behav. 35, 207–220 (2008).Article 

Google Scholar 
Chen, Z. et al. Analysis of social attitude to the new end use of recycled water for household laundry in Australia by the regression models. J. Environ. Manag. 126, 79–84 (2013).Article 

Google Scholar 
Friedler, E. & Lahav, O. Centralised urban wastewater reuse: what is the public attitude. Water Sci. Technol. 54, 423–430 (2006).Article 
CAS 

Google Scholar 
Fielding, K. S., Dolnicar, S. & Schultz, T. Public acceptance of recycled water. Int. J. Water Resour. D 35, 551–586 (2019).Article 

Google Scholar 
Sutherland, C. et al. Socio-technical analysis of a sanitation innovation in a peri-urban household in Durban, South Africa. Sci. Total Environ. 755, 143284 (2021).Article 
CAS 

Google Scholar 
Tyler, T. R. Social justice: outcome and procedure. Int. J. Psychol. 35, 117–125 (2000).Article 

Google Scholar 
Ross, V. L., Fielding, K. S. & Louis, W. R. Social trust, risk perceptions and public acceptance of recycled water: testing a social-psychological model. J. Environ. Manag. 137, 61–68 (2014).Article 

Google Scholar 
Siegrist, M., Connor, M. & Keller, C. Trust, confidence, procedural fairness, outcome fairness, moral conviction, and the acceptance of GM field experiments. Risk Anal. 32, 1394–1403 (2012).Article 

Google Scholar 
Huijts, N. M. A., Contzen, N. & Roeser, S. Unequal means more unfair means more negative emotions? Ethical concerns and emotions about an unequal distribution of negative outcomes of a local energy project. Energy Policy 165, 112963 (2022).Article 

Google Scholar 
Marks, S. J., Onda, K. & Davis, J. Does sense of ownership matter for rural water system sustainability? Evidence from Kenya. J. Water Sanit. Hyg. Dev. 3, 122–133 (2013).Article 

Google Scholar 
Mankad, A. & Tapsuwan, S. Review of socio-economic drivers of community acceptance and adoption of decentralised water systems. J. Environ. Manag. 92, 380–391 (2011).Article 

Google Scholar 
Choukr-Allah, R. in Arab Environment. Water: Sustainable Management of a Scarce Resource (eds El-Ashry, M. et al.) 107–124 (Arab Forum for Environment and Development, 2010).Greenaway, T. & Fielding, K. S. Positive affective framing of information reduces risk perceptions and increases acceptance of recycled water. Environ. Commun. 14, 391–402 (2020).Article 

Google Scholar 
Kraemer, S. M. & Mosler, H.-J. Effectiveness and effects of promotion strategies for behaviour change: solar water disinfection in Zimbabwe. Appl. Psychol. 61, 392–414 (2012).Article 

Google Scholar 
Kirby, M. A. et al. Effects of a large-scale distribution of water filters and natural draft rocket-style cookstoves on diarrhea and acute respiratory infection: a cluster-randomized controlled trial in Western Province, Rwanda. PLoS Med. 16, e1002812 (2019).Article 

Google Scholar 
Trent, M. et al. Access to household water quality information leads to safer water: a cluster randomized controlled trial in india. Environ. Sci. Technol. 52, 5319–5329 (2018).Article 
CAS 

Google Scholar 
John, A. & Orkin, K. Can simple psychological interventions increase preventive health investment? J. Eur. Econ. Assoc. 20, 1001–1047 (2021).Article 

Google Scholar 
Ambuehl, B., Kunwar, B. M., Schertenleib, A., Marks, S. J. & Inauen, J. Can participation promote psychological ownership of a shared resource? An intervention study of community-based safe water infrastructure. J. Environ. Psychol. 81, 101818 (2022).Article 

Google Scholar 
Sheeran, P. & Webb, T. L. The intention–behavior gap. Soc. Pers. Psychol. Compass 10, 503–518 (2016).Article 

Google Scholar 
Pierce, J. L. & Jussila, I. Collective psychological ownership within the work and organizational context: Construct introduction and elaboration. J. Organ. Behav. 31, 810–834 (2010).Article 

Google Scholar 
Pierce, J. L., Kostova, T. & Dirks, K. T. Toward a theory of psychological ownership in organizations. Acad. Manag. Rev. 26, 298–310 (2001).Article 

Google Scholar 
Schwarzer, R. Self-regulatory processes in the adoption and maintenance of health behaviors. J. Health Psychol. 4, 115–127 (1999).Article 
CAS 

Google Scholar 
Schwartz, S. H. & Howard, J. A. in Altruism and Helping Behaviour: Social, Personality, and Developmental Perspectives (eds Rushton, J. P. & Sorrentino, R. M.) 189–211 (Lawrence Erlbaum, 1981).Cialdini, R. B., Kallgren, C. A. & Reno, R. R. A focus theory of normative conduct: a theoretical refinement and reevaluation of the role of norms in human behavior. Adv. Exp. Soc. Psychol. 24, 201–234 (1991).Article 

Google Scholar 
Dreibelbis, R. et al. The integrated behavioural model for water, sanitation, and hygiene: a systematic review of behavioural models and a framework for designing and evaluating behaviour change interventions in infrastructure-restricted settings. BMC Public Health 13, 1015 (2013).Article 

Google Scholar 
Daniel, D., Pande, S. & Rietveld, L. Socio-economic and psychological determinants for household water treatment practices in indigenous–rural Indonesia. Front. Water https://doi.org/10.3389/frwa.2021.649445 (2021).Check, J. & Schutt, R. K. in Research Methods in Education (eds Check, J. & Schutt, R. K.) 141–169 (SAGE Publications, 2012).Reynaert, E., Hess, A. & Morgenroth, E. Making waves: why water reuse frameworks need to co-evolve with emerging small-scale technologies. Water Res. X 11, 100094 (2021).Article 
CAS 

Google Scholar 
Hug, S. J., Winkel, L. H., Voegelin, A., Berg, M. & Johnson, A. C. Arsenic and other geogenic contaminants in groundwater—a global challenge. Chimia 74, 524–524 (2020).Article 
CAS 

Google Scholar 
Safe water enterprises: an entrepreneurial approach to drinking water. Siemens Stiftung https://www.siemens-stiftung.org/en/projects/safe-water-enterprises/ (2023).Lakho, F. H. et al. Decentralized grey and black water reuse by combining a vertical flow constructed wetland and membrane based potable water system: full scale demonstration. J. Environ. Chem. Eng. 9, 104688 (2021).Article 
CAS 

Google Scholar 
Gikas, P. & Tchobanoglous, G. The role of satellite and decentralized strategies in water resources management. J. Environ. Manag. 90, 144–152 (2009).Article 
CAS 

Google Scholar 
Garcia, X. & Pargament, D. Reusing wastewater to cope with water scarcity: economic, social and environmental considerations for decision-making. Resour. Conserv. Recycl. 101, 154–166 (2015).Article 

Google Scholar 
Metcalf & Eddy Inc. an AECOM Company et al. Water Reuse: Issues, Technologies, and Applications (McGraw-Hill Education, 2007).Singh, N. K., Kazmi, A. A. & Starkl, M. A review on full-scale decentralized wastewater treatment systems: techno-economical approach. Water Sci. Technol. 71, 468–478 (2014).Article 

Google Scholar 
Chen, Z., Wu, Q., Wu, G. & Hu, H.-Y. Centralized water reuse system with multiple applications in urban areas: lessons from China’s experience. Resour. Conserv. Recycl. 117, 125–136 (2017).Article 

Google Scholar 
Ambuehl, B. et al. The role of psychological ownership in safe water management: a mixed-methods study in Nepal. Water 13, 589 (2021).Article 

Google Scholar 
Sharma, A. K., Tjandraatmadja, G., Cook, S. & Gardner, T. Decentralised systems—definition and drivers in the current context. Water Sci. Technol. 67, 2091–2101 (2013).Article 

Google Scholar 
O’Driscoll, M. P., Pierce, J. L. & Coghlan, A.-M. The psychology of ownership: work environment structure, organizational commitment, and citizenship behavior. Group Organ. Manag. 31, 388–416 (2006).Article 

Google Scholar 
Marks, S. J. & Davis, J. Does user participation lead to sense of ownership for rural water systems? Evidence from Kenya. World Dev. 40, 1569–1576 (2012).Article 

Google Scholar  More

Test manufactureThe DNA plasmid encoding the fluoride biosensor used in this study was assembled using Gibson assembly (New England Biolabs, Cat#E2611S) and purified using a Qiagen QIAfilter Midiprep Kit (QIAGEN, Cat#12143). Its coding sequence consists of the crcB fluoride riboswitch from Bacillus cereus regulating the production of the enzyme catechol 2,3-dioxygenase, all expressed under the constitutive E. coli sigma 70 consensus promoter J2311939. A complete sequence of the plasmid used is available on Addgene with accession number 128810 (pJBL7025) [https://www.addgene.org/128810/].Cell-free biosensing reactions used in the tests were set up according to previously established protocols20,40. Briefly, reactions consist of cleared cellular extract, a reagent mix containing amino acids, buffering salts, crowding agents, enzymatic substrate, and an energy source, and a reaction-specific mix of template DNA and sodium fluoride in an approximately 30/30/40 ratio (Supplementary Table 3). Test reactions contained no sodium fluoride, while positive control reactions were supplemented with 1 mM sodium fluoride to induce gene expression. Template DNA concentration for both sets of reactions was 5 nM, determined by the maximal template concentration at which no color change was observed in the absence of fluoride.During reaction setup, master mixes of cellular extract, reagent mix, and template mix were prepared for both test and positive control reactions in 1.7 mL microcentrifuge tubes. Individual reactions were then aliquoted into 20 µL volumes in PCR tube strips for lyophilization. After aliquoting on ice, PCR tube caps were pierced with a pin, strips were wrapped in aluminum foil, then the wrapped strips were immersed in liquid nitrogen for freeze-drying for approximately 3 min. Reactions were immediately transferred to a Labconco FreeZone 2.5 Liter −84 °C Benchtop Freeze-Dryer (Cat# 710201000) with a condenser temperature of −84 °C and pressure of 0.04 mbar and freeze-dried overnight (≥16 h).After freeze-drying, tests were vacuum sealed (KOIOS Vacuum Sealer Machine, Amazon, Amazon Standard Identification Number (ASIN) B07FM3J6JF) in a food saver bag (KOIS Vacuum Sealer Bag, Amazon, ASIN B075KKWFYN), along with a desiccant (Dri-Card Desiccants, Uline, Cat# S-19582) (Supplementary Fig. 3). Vacuum sealed reactions were then paced in a light-protective outer bag (Mylar open-ended food bags, Uline, Cat# S-11661) and impulse heat-sealed (Metronic 8-inch Impulse Bag Sealer, Amazon, ASIN B06XC76JVZ) before shipping. Tests were also shipped with single-use 20 µL micropipettes (MICROSAFE® 20 µL, Safe-Tec LLC, Cat# 1020) for field operation.Test-kit shipment to Nakuru County, KenyaA first shipment of biosensor tests was used to assess 33 water samples from the first 16 households surveyed. All of these tests resulted in a faint yellow color, regardless of water source or fluoride concentration established via fluorimeter. This was likely caused by thermal degradation of the tests during shipment with the commercial shipping agency. While previous studies report shelf stability for up to a year20,41, these figures were derived from storage in temperature-controlled laboratory conditions. Commercial shipment routes from Illinois, USA to Nairobi, Kenya pass through extremely hot regions, e.g., Dubai for this particular shipment. These conditions were much different from those in the previous study usability study in Costa Rica in which tests were transported by commercial air, with gentler shipping and storage conditions20. A laboratory investigation of test temperature stability indicated that elevated storage temperatures can indeed cause test components to degrade, resulting in a faint yellow color upon rehydration consistent with field observations (Supplementary Fig. 2).The next batch of tests was therefore shipped refrigerated on January 25th, 2022, which we hypothesized would extend the tests’ shelf stability to align with earlier findings. After the tests were made and packaged, they were placed in a polystyrene foam-lined container before being covered with a NanoCool refrigeration system (Peli BioThermal). The container was then sealed shut and shipped using a standard commercial shipping service. This batch of tests was held in customs, refrigerated, until release on February 28th, 2022. These tests were used in the field from March 5th to March 14th, 2022 to generate the data on test accuracy reported in this manuscript.As discoloration due to thermal degradation could confound the intended yellow hue in the presence of fluoride (i.e., false positives), we assessed test accuracy using only tests that had been refrigerated during shipping and transport to participants’ houses. The 33 water samples from the first 16 households were therefore excluded from analysis of test accuracy.Participant recruitmentParticipants were recruited from six sublocations (Kelelwet, Kipsimbol, Kigonor, Parkview, Lalwet, and Mwariki) in Barut Ward within Nakuru County (Supplementary Fig. 4, geographic information adapted from OpenStreetMap42). This location was chosen because of high fluoride levels and familiarity with the communities by the study team.Before any data were collected, community meetings were held in each sub-location to discuss study goals and objectives. After obtaining permission from the community and village assistant chiefs to conduct research, local community mobilizers were engaged to assist with identifying households eligible for participation. Individuals who were 18 years or older, had lived in Nakuru country for more than three months, relied on local water sources for drinking, had a child in the household, were willing to discuss their household water situation, and provide a sample of each source of water in the household for fluoride testing were eligible. We sought to recruit 10–12 participants from each of the five sublocations to ensure a range of sociodemographic characteristics and drinking water sources. Having a child resident was a criterion in order to elucidate community understandings about fluorosis in children.Data collectionAfter obtaining informed written consent, participants participated in a 30-min survey (cf. Supplementary Fig. 1 for a graphical overview of data collection). Topics included household sociodemographic information, knowledge, attitudes, and behaviors about fluoride and fluorosis, and household water insecurity using the validated Household Water Insecurity Experiences (HWISE) scale43. The 12 HWISE items query the frequency of experiences with water insecurity in the prior month; “never” is scored 0, “often/always” scored 3, for a range of 0–36. These data were collected to be able to investigate if user experiences or attitudes about testing varied by experiences with fluorosis or water insecurity. Participants were also asked about the number of sources of their water and willingness to provide and test water samples. Survey responses were recorded on tablets using Open Data Kit (ODK)44.After completion of the survey, participants provided 1–3 samples of water from different household sources. They then received a brief (~5 min) explanation of the testing process, and then tested their own household samples using the fluoride biosensor tests. Each test consisted of a microtube that was a positive control, and a second microtube in which the sample of interest was tested. To test their samples, participants first removed the tests from the light-protective foil pouch and vacuum sealed pouch containing desiccant, both of which were then discarded (Supplementary Fig. 3). A micropipette was then filled with 20 µL water by slowly immersing it to the fill line. To dispense the water, the thumb and index finger were used to cover the holes in the micropipette while the bulb was squeezed with the other hand. The reactions were then incubated at ambient temperature for up to six hours, shorter if there was a visible color change. During this incubation time, participants were asked to check hourly for yellow color change and note the time taken for it to occur. Tests were expected to turn yellow if fluoride levels were ≥1.5 ppm, with no color change for tests of water below this level. All positive controls were expected to turn yellow. Color change was read after placing reactions against a white background for visual contrast.The study team returned to conduct a second survey on user experiences with the testing process and to test the water samples using the gold-standard photometer within 6 h. Participants were asked about their experiences with the testing procedure as well as their interpretation of the color of the results of the sample and control tests. Photographs of the completed reactions were also taken at this time. Finally, quantitative fluoride measurements were taken by the field team with a Hanna Instruments Fluoride High Range Photometer Kit (Cat# HI97739C), a gold-standard method used to assess the accuracy of the bioengineered tests. Photometry results on actual measured fluoride concentrations of water samples were shared with and explained to participants. At the conclusion of the second survey, each participant was given KES 500 (USD 4.30) as remuneration for the time and effort spent participating in the research. Each participating household was also given a ceramic drinking water filter.Data were collected from November 16th to November 23rd, 2021 and March 5th to March 14th, 2022. During surveying and water testing, participants and research assistants maintained COVID-19 protocols as per the local area guidelines. Study staff were vaccinated, maintained appropriate social distancing, sanitized hands, and cleaned field tools after each household visit.Data analysisData were exported from ODK into Microsoft Excel for analysis. Basic descriptive statistics were performed to describe participant socio-demographics and experiences with usability, including if participants’ interpretation of color change matched that of study staff. Open-ended items about fluoride and fluorosis knowledge, attitudes, and behavior were grouped thematically and coded independently by two authors. Knowledge-related responses were characterized as “correct” if consistent with conventional biomedical understanding, “incorrect”, or unfamiliar.Tests were classified as ‘ON’ by the Kenya-based field team if they were visibly yellow after six hours, and ‘OFF’ if there was no observable color change by eye. These assessments were independently validated by the US-based team from photographs of the completed tests. Tests classified as ‘ON’ were marked true positive if they corresponded to a photometer measured fluoride concentration ≥1.5 ppm, and false positive if they corresponded to a photometer measured fluoride concentration More

Smil, V. IEEE Spectrum 55, 72–75 (2018).Ritchie, H., Roser, M. & Rosado, P. CO2 and greenhouse gas emissions. Our World in Data (2020); https://go.nature.com/3iO9QSvHenry, A., Prasher, R. & Majumdar, A. Nat. Energy 5, 635–637 (2020).Article 
CAS 

Google Scholar 
Caldera, U. & Breyer, C. Nat. Sustain. https://doi.org/10.1038/s41893-022-01056-7 (2023).Article 

Google Scholar 
Bastin, J.-F. et al. Science 365, 76–79 (2019).Article 
CAS 

Google Scholar 
Child, M. et al. Renew. Energy 139, 80–101 (2019).Article 

Google Scholar 
Crippa, M. et al. CO2 emissions of all world countries (Joint Research Centre (European Commission), 2022); https://doi.org/10.2760/730164Urbina, A. in Sustainable Solar Electricity 131–155 (Springer Cham, 2022).Yu, H. F. et al. Sustainability 14, 8567 (2022).Article 

Google Scholar 
Caldera, U. & Breyer, C. Energy 200, 117507 (2020).Article 

Google Scholar  More

Land area for afforestation with RE-based SWRO desalinationRestoration land37 and bare land areas22 with the following water stress conditions were determined to be areas where forests could grow if irrigated with a secure water supply. The projected water stress, water supply and demand data for the decade 2040 are used. The renewable water resources in these areas were not considered sufficient to sustain forest growth.

Land nodes that lie in high (40%  More

Zhu, S. et al. Distinct impacts of spring soil moisture over the Indo-China Peninsula on summer precipitation in the Yangtze River basin under different SST backgrounds. Clim. Dyn. 56, 1895–1918 (2021).
Google Scholar 
Shi, P. et al. Significant land contributions to interannual predictability of East Asian summer monsoon rainfall. Earth’s Futur 9, 1–16 (2021).
Google Scholar 
Wu, G. et al. Thermal controls on the Asian summer monsoon. Sci. Rep. 2, 404 (2012).
Google Scholar 
Wei, W., Zhang, R., Wen, M., Rong, X. & Li, T. Impact of Indian summer monsoon on the South Asian High and its influence on summer rainfall over China. Clim. Dyn. 43, 1257–1269 (2014).
Google Scholar 
Wang, B., Xiang, B. & Lee, J. Y. Subtropical High predictability establishes a promising way for monsoon and tropical storm predictions. Proc. Natl Acad. Sci. USA 110, 2718–2722 (2013).
Google Scholar 
Kundzewicz, Z. W. et al. Climate variability and floods in China—a review. Earth-Sci. Rev. 211, 103434 (2020).
Google Scholar 
Wang, C., Yang, K., Li, Y., Wu, D. & Bo, Y. Impacts of spatiotemporal anomalies of Tibetan plateau snow cover on summer precipitation in Eastern China. J. Clim. 30, 885–903 (2017).
Google Scholar 
Ding, T. & Gao, H. Relationship between winter snow cover days in Northeast China and rainfall near the Yangtze river basin in the following summer. J. Meteorol. Res. 29, 400–411 (2015).
Google Scholar 
Kundzewicz, Z. W. et al. Flood risk and its reduction in China. Adv. Water Resour. 130, 37–45 (2019).
Google Scholar 
Jiang, T. et al. Each 0.5 °C of warming increases annual flood losses in China by more than US$60 billion. Bull. Am. Meteorol. Soc. 101, E1464–E1474 (2021).
Google Scholar 
Su, B. et al. Drought losses in China might double between the 1.5 °C and 2.0 °C warming. Proc. Natl Acad. Sci. USA 115, 10600–10605 (2018).
Google Scholar 
Li, Z., Sun, Y., Li, T., Ding, Y. & Hu, T. Future Changes in East Asian Summer Monsoon Circulation and Precipitation Under 1.5 to 5 °C of Warming. Earth’s Futur 7, 1391–1406 (2019).
Google Scholar 
Zhang, R. H. Natural and human-induced changes in summer climate over the East Asian Monsoon region in the last half century: a review. Adv. Clim. Chang. Res. 6, 131–140 (2015).
Google Scholar 
Almazroui, M. et al. Projected changes in climate extremes using CMIP6 simulations over SREX regions. Earth Syst. Environ. 5, 481–497 (2021).
Google Scholar 
Huang, J. J., Zhang, N., Choi, G., McBean, E. A. & Zhang, Q. Spatiotemporal patterns and trends of precipitation and their correlations with related meteorological factors by two sets of reanalysis data in China. Hydrol. Earth Syst. Sci. Discuss 5, 1–35 (2018).
Google Scholar 
Ha, K. J., Heo, K. Y., Lee, S. S., Yun, K. S. & Jhun, J. G. Variability in the East Asian Monsoon: a review. Meteorol. Appl. 19, 200–215 (2012).
Google Scholar 
Wang, P. X. et al. The global monsoon across time scales: Mechanisms and outstanding issues. Earth-Sci. Rev. 174, 84–121 (2017).
Google Scholar 
Wu, G. et al. The influence of mechanical and thermal forcing by the Tibetan Plateau on Asian climate. J. Hydrometeorol. 8, 770–789 (2007).
Google Scholar 
Abe, M., Hori, M., Yasunari, T. & Kitoh, A. Effects of the Tibetan Plateau on the onsetof the summer monsoon in South Asia: The role of the air-sea interaction. J. Geophys. Res. Atmos. 118, 1760–1776 (2013).
Google Scholar 
Abbas, A., Waseem, M., Ullah, W., Zhao, C. & Zhu, J. Spatiotemporal analysis of meteorological and hydrological droughts and their propagations. Water 13, 2237 (2021).
Google Scholar 
Zhu, C., Lee, W. S., Kang, H. & Park, C. K. A proper monsoon index for seasonal and interannual variations of the East Asian Monsoon. Geophys. Res. Lett. 32, 1–5 (2005).
Google Scholar 
Wu, L. & Zhang, J. The relationship between spring soil moisture and summer hot extremes over North China. Adv. Atmos. Sci. 32, 1660–1668 (2015).
Google Scholar 
Gao, C. et al. Land–atmosphere interaction over the Indo-China Peninsula during spring and its effect on the following summer climate over the Yangtze River basin. Clim. Dyn. 53, 6181–6198 (2019).
Google Scholar 
Wang, B. & Fan, Z. Choice of South Asian summer monsoon indices. Bull. Am. Meteorol. Soc. 80, 629–638 (1999).
Google Scholar 
Lv, A., Qu, B., Jia, S. & Zhu, W. Influence of three phases of El Niño-Southern Oscillation on daily precipitation regimes in China. Hydrol. Earth Syst. Sci. 23, 883–896 (2019).
Google Scholar 
Wu, Z., Li, J., Jiang, Z. & Ma, T. Modulation of the Tibetan Plateau snow cover on the ENSO teleconnections: from the East Asian summer monsoon perspective. J. Clim. 25, 2481–2489 (2012).
Google Scholar 
Liu, D., Wang, G., Mei, R., Yu, Z. & Yu, M. Impact of initial soil moisture anomalies on climate mean and extremes over Asia. J. Geophys. Res. 119, 529–545 (2014).
Google Scholar 
Ullah, W. et al. Observed linkage between Tibetan plateau soil moisture and South Asian summer precipitation and the possible mechanism. J. Clim. 34, 361–377 (2021).
Google Scholar 
Koster, R. D., Chang, Y., Wang, H. & Schubert, S. D. Impacts of local soil moisture anomalies on the atmospheric circulation and on remote surface meteorological fields during boreal summer: a comprehensive analysis over North America. J. Clim. 29, 7345–7364 (2016).
Google Scholar 
Seneviratne, S. I. et al. Investigating soil moisture-climate interactions in a changing climate: a review. Earth-Sci. Rev. 99, 125–161 (2010).
Google Scholar 
Mei, R. & Wang, G. Impact of sea surface temperature and soil moisture on summer precipitation in the united states based on observational data. J. Hydrometeorol. 12, 1086–1099 (2011).
Google Scholar 
Alessandri, A. & Navarra, A. On the coupling between vegetation and rainfall inter-annual anomalies: possible contributions to seasonal rainfall predictability over land areas. Geophys. Res. Lett. 35, 1–6 (2008).
Google Scholar 
Dorigo, W. et al. ESA CCI soil moisture for improved Earth system understanding: state-of-the art and future directions. Remote Sens. Environ. 203, 185–215 (2017).
Google Scholar 
Santanello, J. A. et al. Land-atmosphere interactions the LoCo perspective. Bull. Am. Meteorol. Soc. 99, 1253–1272 (2018).
Google Scholar 
Rasmijn, L. M. et al. Future equivalent of 2010 Russian heatwave intensified by weakening soil moisture constraints. Nat. Clim. Chang. 8, 381–385 (2018).
Google Scholar 
Denissen, J. M. C. et al. Soil moisture signature in global weather balloon soundings. npj Clim. Atmos. Sci. 4, 13 (2021).
Google Scholar 
Lau, W. K. M. & Kim, K. M. The 2010 Pakistan flood and Russian heat wave: teleconnection of hydrometeorological extremes. J. Hydrometeorol. 13, 392–403 (2012).
Google Scholar 
Mann, M. E. et al. Influence of anthropogenic climate change on planetary wave resonance and extreme weather events. Sci. Rep. 7, 12 (2017).
Google Scholar 
Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C. & De Arellano, J. V. G. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7, 345–349 (2014).
Google Scholar 
Dong, X., Zhou, Y., Chen, H., Zhou, B. & Sun, S. Lag impacts of the anomalous July soil moisture over Southern China on the August rainfall over the Huang–Huai River Basin. Clim. Dyn. 58, 1737–1754 (2022).
Google Scholar 
Bao, Q., Liu, Y., Shi, J. & Wu, G. Comparisons of soil moisture datasets over the Tibetan Plateau and application to the simulation of Asia summer monsoon onset. Adv. Atmos. Sci. 27, 303–314 (2010).
Google Scholar 
Meng, X. et al. Detecting hydrological consistency between soil moisture and precipitation and changes of soil moisture in summer over the Tibetan Plateau. Clim. Dyn. 51, 4157–4168 (2018).
Google Scholar 
Wei, J. & Dirmeyer, P. A. Sensitivity of land precipitation to surface evapotranspiration: a nonlocal perspective based on water vapor transport. Geophys. Res. Lett. 46, 12588–12597 (2019).
Google Scholar 
Wei, J. & Dirmeyer, P. A. Dissecting soil moisture-precipitation coupling. Geophys. Res. Lett. 39, 1–6 (2012).
Google Scholar 
Kim, Y. & Wang, G. Soil moisture-vegetation-precipitation feedback over North America: its sensitivity to soil moisture climatology. J. Geophys. Res. Atmos. 117, 1–18 (2012).
Google Scholar 
Ullah, W., Wang, G., Gao, Z., Hagan, D. F. T. & Lou, D. Comparisons of remote sensing and reanalysis soil moisture products over the Tibetan Plateau, China. Cold Reg. Sci. Technol. 146, 110–121 (2018).
Google Scholar 
Samuel, J., Coulibaly, P., Dumedah, G. & Moradkhani, H. Assessing model state and forecasts variation in hydrologic data assimilation. J. Hydrol. 513, 127–141 (2014).
Google Scholar 
Koster, R. D. et al. Regions of strong coupling between soil moisture and precipitation. Science 305, 1138–1140 (2004).
Google Scholar 
Navarra, A. & Tribbia, J. The coupled manifold. J. Atmos. Sci. 62, 310–330 (2005).
Google Scholar 
Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World map of the Köppen–Geiger climate classification updated. Meteorol. Z. 15, 259–263 (2006).
Google Scholar 
Liu, B. et al. Asian summer monsoon onset barrier and its formation mechanism. Clim. Dyn. 45, 711–726 (2015).
Google Scholar 
Liu, B., Wu, G., Mao, J. & He, J. Genesis of the South Asian high and its impact on the Asian summer monsoon onset. J. Clim. 26, 2976–2991 (2013).
Google Scholar 
Li, J. et al. How to measure the strength of the East Asian Summer monsoon. J. Clim. 21, 4449–4463 (2008).
Google Scholar 
Wang, B., LinHo, Zhang, Y. & Lu, M. M. Definition of South China Sea monsoon onset and commencement of the East Asian summer monsoon. J. Clim. 17, 699–710 (2004).
Google Scholar 
Xing, N., Li, J. & Wang, L. Effect of the early and late onset of summer monsoon over the Bay of Bengal on Asian precipitation in May. Clim. Dyn. 47, 1961–1970 (2016).
Google Scholar 
Khan, A. A. et al. Spatial and temporal analysis of rainfall and drought condition in Southwest Xinjiang in Northwest China, using various climate indices. Earth Syst. Environ. 5, 201–216 (2021).
Google Scholar 
Zhang, Z., Sun, X. & Yang, X.-Q. Understanding the interdecadal variability of East Asian summer monsoon precipitation: joint influence of three oceanic signals. J. Clim. 31, 5485–5506 (2018).
Google Scholar 
Liu, L., Zhang, R. & Zuo, Z. Effect of spring precipitation on summer precipitation in Eastern China: role of soil moisture. J. Clim. 30, 9183–9194 (2017).
Google Scholar 
Chahine, M. T. The hydrological cycle and its influence on climate. Nature 359, 373–380 (1992).
Google Scholar 
Zhang, R. & Zuo, Z. Impact of spring soil moisture on surface energy balance and summer monsoon circulation over East Asia and precipitation in East China. J. Clim. 24, 3309–3322 (2011).
Google Scholar 
Berg, A., Lintner, B., Findell, K. & Giannini, A. Soil moisture influence on seasonality and large-scale circulation in simulations of the West African monsoon. J. Clim. 30, 2295–2317 (2017).
Google Scholar 
Taylor, C. M. et al. New perspectives on land-atmosphere feedbacks from the African monsoon multidisciplinary analysis. Atmos. Sci. Lett. 12, 38–44 (2011).
Google Scholar 
Zuo, Z. & Zhang, R. Influence of soil moisture in eastern China on the East Asian summer monsoon. Adv. Atmos. Sci. 33, 151–163 (2016).
Google Scholar 
Yang, K., Wang, C. & Bao, H. Contribution of soil moisture variability to summer precipitation in the northern hemisphere. J. Geophys. Res. 121, 12,108–12,214 (2016).
Google Scholar 
Min, J., Guo, Y. & Wang, G. Impacts of soil moisture on typical frontal rainstorm in Yangtze River Basin. Atmosphere 7, 0–24 (2016).
Google Scholar 
Zhu, B., Xie, X., Meng, S., Lu, C. & Yao, Y. Sensitivity of soil moisture to precipitation and temperature over China: present state and future projection. Sci. Total Environ. 705, 135774 (2020).
Google Scholar 
Cheng, S., Guan, X., Huang, J., Ji, F. & Guo, R. Long-term trend and variability of soil moisture over East Asia. J. Geophys. Res. 120, 8658–8670 (2015).
Google Scholar 
AbdelRahman, M. A. E. & Arafat, S. M. An approach of agricultural courses for soil conservation based on crop soil suitability using geomatics. Earth Syst. Environ. 4, 273–285 (2020).
Google Scholar 
Liu, Y. et al. Agriculture intensifies soil moisture decline in Northern China. Sci. Rep. 5, 11261 (2015).
Google Scholar 
Yuan, Q. et al. Coupling of soil moisture and air temperature from multiyear data during 1980–2013 over china. Atmosphere 11, 0–14 (2020).
Google Scholar 
Xu, Z., Chen, H., Guo, J. & Zhang, W. Contrasting effect of soil moisture on the daytime boundary layer under different thermodynamic conditions in summer over China. Geophys. Res. Lett. 48, 1–11 (2021).
Google Scholar 
Xia, K., Li, L., Tang, Y. & Wang, B. Impact of soil freezing-thawing processes on August rainfall over Southern China. J. Geophys. Res. Atmos. 127, 1–16 (2022).
Google Scholar 
Gu, X. et al. Extreme precipitation in China: a review on statistical methods and applications. Adv. Water Resour. 163, 104144 (2022).
Google Scholar 
Karthikeyan, L., Pan, M., Wanders, N., Kumar, D. N. & Wood, E. F. Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons. Adv. Water Resour. 109, 236–252 (2017).
Google Scholar 
Yuan, Z., Yang, Z., Yan, D. & Yin, J. Historical changes and future projection of extreme precipitation in China. Theor. Appl. Climatol. 127, 393–407 (2017).
Google Scholar 
Ren, Z. et al. Changes in daily extreme precipitation events in South China from 1961 to 2011. J. Geogr. Sci. 25, 58–68 (2015).
Google Scholar 
Dorigo, W. A. et al. Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sens. Environ. 162, 380–395 (2015).
Google Scholar 
Wang, G., Garcia, D., Liu, Y., de Jeu, R. & Dolman, A. J. A three-dimensional gap filling method for large geophysical datasets: application to global satellite soil moisture observations. Environ. Model. Softw. 30, 139–142 (2012).
Google Scholar 
Hersbach, H. et al. Global reanalysis: goodbye ERA-Interim, hello ERA5. ECMWF Newsl. 17–24 (2019).Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
Google Scholar 
Hagan, D. F. T., Parinussa, R. M., Wang, G. & Draper, C. S. An evaluation of soil moisture anomalies from global model-based datasets over the People’s Republic of China. Water 12, 1–15 (2020).
Google Scholar 
Richman, M. B. & Vermette, S. J. The use of procrustes target analysis to discriminate dominant source regions of fine sulfur in the western USA. Atmos. Environ. Part A. Gen. Top. 27, 475–481 (1993).
Google Scholar 
Wang, G., Dolman, A. J. & Alessandri, A. A summer climate regime over Europe modulated by the North Atlantic Oscillation. Hydrol. Earth Syst. Sci. 15, 57–64 (2011).
Google Scholar 
Catalano, F., Alessandri, A., De Felice, M., Zhu, Z. & Myneni, R. B. Observationally based analysis of land-atmosphere coupling. Earth Syst. Dyn. 7, 251–266 (2016).
Google Scholar 
Hannachi, A. A primer for EOF analysis of climate data. (United Kingdom: Department of Meteorology, University of Reading, 2004).Lund, R. B., von Storch, H. & Zwiers, F. W. Statistical analysis in climate research. J. Am. Stat. Assoc. 95, 1375 (2000).
Google Scholar 
Preisendorfer, R. W. Principal Component Analysis in Meteorology and Oceanography XVIII, 425 (Elsevier; Distributors for the U.S. and Canada, Elsevier Science Pub. Co., 1988).Krishnamurti, T. N. Tropical East-West circulations during the Northern summer. J. Atmos. Sci. 28, 1342–1347 (1971).
Google Scholar 
Mancuso, R. L. A numerical procedure for computing fields of stream function and velocity potential. J. Appl. Meteorol. 6, 994–1001 (1967).
Google Scholar 
Kulkarni, P. L., Mitra, A. K., Narkhedkar, S. G., Bohra, A. K. & Rajamani, S. On the impact of divergent part of the wind computed from INSAT OLR data on global analysis and forecast fields. Meteorol. Atmos. Phys. 64, 61–82 (1997).
Google Scholar 
Wei, J., Su, H. & Yang, Z. L. Impact of moisture flux convergence and soil moisture on precipitation: a case study for the southern United States with implications for the globe. Clim. Dyn. 46, 467–481 (2016).
Google Scholar 
Pal, J. S. et al. Regional Climate Modeling for the Developing World: The ICTP RegCM3 and RegCNET. Bull. Am. Meteorol. Soc. 88, 1395–1410 (2007).
Google Scholar 
Dickinson, R. E., Henderson-Sellers, A. & Kennedy, P. J. Biosphere-atmosphere Transfer Scheme (BATS) Version 1e as Coupled to the NCAR Community Climate Model (No. NCAR/TN-387+STR). (University Corporation for Atmospheric Research, 1993). https://doi.org/10.5065/D67W6959.Emanuel, K. A. A scheme for representing cumulus convection in large-scale models. J. Atmos. Sci. 48, 2313–2329 (1991).
Google Scholar 
Pal, J. S., Small, E. E. & Eltahir, E. A. B. Simulation of regional-scale water and energy budgets: representation of subgrid cloud and precipitation processes within RegCM. J. Geophys. Res. Atmos. 105, 29579–29594 (2000).
Google Scholar 
Dirmeyer, P. A., Zeng, F. J., Ducharne, A., Morrill, J. C. & Koster, R. D. The sensitivity of surface fluxes to soil water content in three land surface schemes. J. Hydrometeorol. 1, 121–134 (2000).
Google Scholar 
Wei, J., Dickinson, R. E. & Chen, H. A negative soil moisture–precipitation relationship and its causes. J. Hydrometeorol. 9, 1364–1376 (2008).
Google Scholar 
Bisselink, B., van Meijgaard, E., Dolman, A. J. & de Jeu, R. A. M. Initializing a regional climate model with satellite-derived soil moisture. J. Geophys. Res. Atmos. 116, 1–13 (2011).Yang, K. & Wang, C. Seasonal persistence of soil moisture anomalies related to freeze–thaw over the Tibetan Plateau and prediction signal of summer precipitation in eastern China. Clim. Dyn. 53, 2411–2424 (2019).
Google Scholar 
Dickinson, R. E., Errico, R. M., Giorgi, F. & Bates, G. T. A regional climate model for the western United States. Clim. Change 15, 383–422 (1989).
Google Scholar 
Taylor, K. E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos. 106, 7183–7192 (2001).
Google Scholar  More