what would you be most likely to find if you returned to the solar system in 10.0 billion years?

Abstract

Access to safely managed drinking h2o (SMDW) remains a global claiming, and affects 2.two billion people^1,2. Solar-driven atmospheric water harvesting (AWH) devices with continuous cycling may accelerate progress by enabling decentralized extraction of water from air^3,4,v,6, only low specific yields (SY) and low daytime relative humidity (RH) take raised questions about their functioning (in litres of h2o output per day)^{7,8,9,10,eleven}. Nevertheless, to our knowledge, no analysis has mapped the global potential of AWH¹² despite favourable conditions in tropical regions, where two-thirds of people without SMDW live². Hither we testify that AWH could provide SMDW for a billion people. Our assessment—using Google Earth Engine¹³—introduces a hypothetical ane-metre-foursquare device with a SY profile of 0.2 to 2.five litres per kilowatt-hour (0.one to 1.25 litres per kilowatt-hour for a 2-metre-square device) at 30% to 90% RH, respectively. Such a device could meet a target boilerplate daily drinking water requirement of 5 litres per mean solar day per person¹⁴. We plot the impact potential of existing devices and new sorbent classes, which suggests that these targets could be met with connected technological evolution, and well within thermodynamic limits. Indeed, these performance targets take been achieved experimentally in demonstrations of sorbent materials^15,16,17. Our tools can inform design trade-offs for atmospheric water harvesting devices that maximize global touch, alongside ongoing efforts to meet Sustainable Development Goals (SDGs) with existing technologies.

Main

Ensuring reliable access to safe drinking water for all remains a global challenge, and is formally recognized as an international development priority past 2030 in the United nations framework for global development priorities, the Sustainable Development Goals 6.ane^xviii. Progress towards this target is measured by the WHO/UNICEF Joint Monitoring Programme (JMP) as the per centum of population using safely managed drinking h2o (SMDW), where 'safely managed' is defined as "an improved source located on the premises, available when needed and free of fecal and priority chemic contamination"^1,2. Traditional routes to bring SMDW on premises to currently unserved populations are estimated to cost US$114 billion per year (from 2015), more than three times the historical financing trend¹⁹. Moreover, there is increasing global involvement in solutions that provide safe drinking h2o without the environmental consequences of increasing reliance on bottled h2o and that practice not require household-level intervention, which has limited adherence^20,21. Atmospheric water harvesting (AWH) shows promise to accelerate decentralized access to underserved communities if a cost-effective, off-filigree device can be designed and scaled⁶.

Several classes of off-filigree AWH designs exist or are being explored^{eight,12,22,23}, as summarized in Table one. AWH devices are categorized by free energy source—active devices use external energy sources whereas passive devices rely solely on atmospheric weather that allow for pre-condensed dew or fog to be harvested. Passive devices are thus limited to geographic niches where dew or fog tin can be systematically harvested^7,12,24. Active, sorbent-based AWH devices extract water using primarily solar thermal energy in i of two operational modes: diurnal-mode devices excerpt at night (when RH is college) and condense during the day (when solar energy is available) in a single daily cycle, requiring a large sorbent bed. Past dissimilarity, continuous-mode devices are not express to a unmarried daily cycle, and need only hold a pocket-size amount of h2o vapour in-process^3,iv, drastically reducing sorbent mass and device size. This, yet, requires extraction at lower RH when solar free energy is available, raising questions about performance^{seven,viii,9,10,11}. Cooler–condenser devices utilize piece of work (typically electric energy) to actively absurd air below its dew signal and collect condensation and—if solar-driven—call for photovoltaic (PV) panels. Unlike solar–thermal devices, solar-driven cooler–condenser devices endure from a steep loss in electric energy conversion. In the context of specific yield, we use kWh to denote primary solar energy prior to thermal and other losses, and kWh_PV to denote electrical free energy supplied to the device from PV panels afterward conversion. Unless stated otherwise, ranges of SY refer to RH between thirty% and 90% at 20 °C.

Table one Suitability of household-calibration applications by AWH category

Total size table

Here we present an assessment of solar-driven, continuous-mode AWH (SC-AWH) using global information. AWH has much lower SY than infrastructural h2o sources such as desalination²⁵ (approximately 200 fifty kWh⁻ⁱ). However, SC-AWH devices sized to produce sufficient daily drinking water output for an individual or family could address both the h2o quality and the water access dimensions of SMDW solutions at the household level.

Geography of the global challenge

To estimate the bear on potential of SC-AWH, we starting time mapped the distribution of the approximately ii.two billion people without SMDW². Contempo studies accept used geostatistical techniques to estimate subnational inequalities of condom h2o and sanitation from a variety of data sources reporting metrics of facility blazon^26,27. Here we use a deterministic method based exclusively on JMP data on drinking h2o service levels. In this study, we assume that SC-AWH is for drinking h2o only and does non supercede water for other domestic uses such as hygiene, cooking and sanitation^14,28.

The overall percentage of the population in regions reported by the JMP at the everyman respective available regional bureaucracy is shown in Fig. 1a. This seamless material of national and subnational survey regions gives a spatially continuous moving-picture show of the global distribution of people living without SMDW. Sub-Saharan Africa contains the highest total number of people without SMDW, in alignment with previous reports^ii,29, followed by regions in Southern asia and Latin America.

**Fig. 1: Geographic distribution of world population without SMDW.**

The regional proportions from Fig. 1a were applied as a linear weight to each pixel of the WorldPop (2017) 1 km-resolution residential population counts image (https://www.worldpop.org). This gives an judge of the distribution of people without SMDW to a spatial resolution that more closely matches the scales at which climate variables relevant for AWH vary attributable to physical geography, such equally topography and state cover. The resulting weighted population distribution is shown in Fig. 1b.

Geospatial toolset for AWH assessment

We present a geospatial tool (AWH-Geo) for assessing the global potential for notional SC-AWH devices given available climatic resource. AWH-Geo was built in Google World Engine¹³ and is extensible beyond climate data. For this study, AWH-Geo uses the ERA5-Land climate reanalysis over the 10-year menstruum 2010–2019 (inclusive). ERA5-Land was called for its fine resolution (ix km at hourly intervals), global coverage and ability to represent historical synoptic conditions. This period is sufficient to business relationship for interannual variability, although decadal trends are explored in cursory in Extended Information Fig. nine. For shorter computation times running their own analysis, the user can arrange the analysis menstruation within the tool.

AWH-Geo takes as input the instantaneous rate of water output as a function of the three dominant environmental variables: (1) global horizontal irradiance from sunlight (GHI (in Westward m⁻²)), (ii) RH (%) and (3) air temperature (T (°C)). Secondary climate variables could be incorporated afterwards (for instance, downwelling infrared and surface wind speed). We propose an output table with h2o yield values equally a role of binned climate inputs GHI, RH and T, equally a way to connect AWH device models or experimental characterizations with geospatial analyses. Water output tin exist entered in areal harvesting rates (in l h^−ane m⁻²) for abstractions, or as the expected yield of a existent device with known collection areas (in l h⁻ⁱ). Across all information points of a multi-twelvemonth climate epitome time series, AWH-Geo uses the given output table to look up yield values and aggregates water outputs for display equally global maps or derived plots. Whereas previous assessments have been limited to relatively small numbers of locations with on-site meteorological information^7,xxx or express the assay to a region³¹, the approach presented here is global and spatially continuous. Effigy 2 shows a conceptual workflow of AWH-Geo and adjacent processes to produce results in this report.

**Fig. 2: Information processing workflow of AWH-Geo.**

We kickoff used AWH-Geo to map theoretical upper bounds of solar-driven AWH by constructing output tables from the literature as specific water yields SY (in l kWh⁻¹). SY is an evaluative metric for AWH sensitive to RH³², and is the inverse of specific energy consumption (SEC), which is usually used for other water and desalination systems. Resulting maps are overlaid with a dot-density representation of the distribution of people without SMDW for visual comparing in Fig. 3.

**Fig. 3: Upper premises h2o output of solar-driven AWH in relation to global user base.**

Recently, Kim et al. take described the fundamental thermodynamic limits for AWH³³. This model gives the minimum thermal energy required (at a given hot-side temperature level) per unit water output of a black box AWH, corresponding to SY values betwixt 5 and 50 l kWh⁻¹. Kim's thermodynamic limits are mapped in Fig. 3a. Mapping thermodynamic limits is useful to prepare maximum expectations for SC-AWH output globally and to assess the improvement potential that may be between existing device performance and fundamental physical limits. Similar analytic approaches have been used to assess condenser-based devices, diurnal devices and dew collectors practical to a specific location or region^7,12,30,31. The geographic patterns of output closely follow time-averaged humidity values generally, modified by the availability of sunlight. Notably, the results show significant h2o product potential throughout much of the world, particularly in the tropics.

Adjacent, we mapped the maximum output of two bones pattern types. Peeters describes the maximum yield for active cooler–condensers³², giving SYs of one–30${\rm{fifty}}\,{{\rm{kWh}}}_{{\rm{PV}}}^{-1}$ (0.2–6 l kWh^−ane), plotted using AWH-Geo in Fig. 3b. For sorbent designs, metal organic frameworks (MOFs) and thermo-responsive polymer (TRP) gels¹⁷ show the highest yields at low and high RH, respectively. Zhao et al. demonstrated infrequent performance of a TRP^xv at high RH (0.2–nine.three l kWh⁻¹ (converted to SY by Peeters³²)), generally outperforming MOFs (whose reported maximum³² SYs are around ane 50 kWh⁻¹). Global projections for Zhao'south TRP are mapped in Fig. 3c.

In add-on to annual ways, AWH-Geo is capable of deriving metrics useful for analysing seasonal variability of output. Optionally, AWH-Geo exports 90% availability (P90) values across a gear up of time windows (Methods).

Assessing the global potential

Our coincidence assay calculates the mean hours per day during which GHI and RH are simultaneously above parametric thresholds. Fig. 4a maps almanac means for such daily coincidence hours for the given threshold pairs, interpreted as the operational hours per day (ophd) for a hypothetical device. Important transition areas between tropical and desert regions show the expected trade-off between sunlight and humidity, which mostly vary inversely. Very low RH thresholds of 10% increment ophd potential by only ane–2 h from the ophd at 30% RH in arid regions in the Sahel across GHI thresholds, simply ophd so falls sharply at college RH thresholds. This indicates a diminishing return to devices operating below xxx%. Coastal areas show promise for consequent 2–4 ophd worldwide above l% RH.

**Fig. 4: AWH engineering parameters in relation to global affect targets.**

Next, we summed the population without access to SMDW segmented by threshold pair using the weighted population image, grouped cumulatively by ophd at whole intervals and shown in Fig. 4b. Inflections of diminishing user potential occur between values of RH between 30 and 50%, GHI between 400 and 600 W m⁻ⁱⁱ and ophd betwixt iii and v h. These reflect key spatio-demographic patterns along similar climatic transitions in the torrid zone, where the bulk of those living without SMDW live—particularly in the tropical savanna of sub-Saharan Africa and the Ganges River Valley in Republic of india. A device that could operate in a higher place these values has the theoretical potential to serve more than than one-half the earth's remaining population lacking access to SMDW.

Side by side we ran the SY profiles of a collection of SY curves through AWH-Geo, including commercial cooler–condenser devices evaluated past Bagheri³⁴ and a data sheet for the SOURCE panel, a sorbent-based device from company SOURCE, formerly known equally Nada Mass H2o³⁵ (ZMW).

Figure 4c shows resulting outputs normalized by surface area (in 50 d⁻ⁱ yard⁻²)—a functioning metric advocated by LaPotin et al. ¹¹—as a function of the population without SMDW reached. Steep gradients of the human bear upon of the output mirror those in the coincidence assay. Linear SY profiles prioritize functioning at low RH, but cap output even in resource-rich climates. The target curves are based on hypothetical SY values like to those characteristic of sorbent or device profiles that reach 1 billion users at an average of 5 l d⁻¹ thou⁻ⁱⁱ. Comparing the two target curves demonstrates the expected trade-off between serving more than users at depression output (linear) and fewer users at high output (logistic).

To further explore trade-offs of the SY curve across different values of RH, nosotros plotted SY values from materials and devices in relation to target curves for reaching 0.5–2.0 billion people without SMDW at 5 l d^−ane, the gauge daily drinking h2o requirements of an individual¹⁴ (Fig. 4d). We based the target curves on a 1 m² device unless otherwise noted, although h2o output and SY targets scale linearly with device surface area in sunlight. To demonstrate this, we plotted a version of the 1.0-billion target based on two g²—this doubling of the device area halves the SY requirements for the target impacts. The existing devices both follow approximately linear yields beyond RH below the 0.v-billion impact target curves. MOFs and other sorbents show varied results^3,36, although they remain roughly linear. Zhao'due south exceptional yields at loftier RH make up for low performance at low RH (logistic profile), and show the most promise for reaching the largest user base (2.0 billion). Effigy 4d compares material and device performance side-by-side to show the gap between present capabilities and theoretical limits, although real devices volition be bailiwick to losses that will prevent them from fully reaching arcadian fabric performance or theoretical limits.

Closing the gap

This study presents initial conclusions—developing detailed SC-AWH design criteria will require further work. A device with a one m² solar collection area and a SY profile of 0.ii–2.five l kWh^−one (0.1–1.25 l kWh^−ane for 2 chiliad²) tin can serve the SMDW needs of about 1 billion people, assuming continuous harvesting of 2–3 h per day of coincident sunlight of more than 600 West one thousand⁻² and RH above 30%. The shape of the SY bend is disquisitional for SC-AWH to take reward of coincident humidity and solar energy during key periods of the day, typically during morning and evening hours. A trade-off exists between increasing yields at lower RH (around 30%) for those in climate transition zones (northern sub-Saharan Africa and western Bharat), versus focusing on exponentially higher yields in humid regions such as Bangladesh and equatorial regions.

Researchers and device inventors tin cantankerous-reference Fig. 4 when making trade-off decisions between sets of technical specifications and servable regions and people. Recent experiments^4,5,37 evidence rapid improvements in multi-cycled sorption material yield, ranging from 0.1 to more than 8.0 fifty d⁻¹ kg⁻¹ sorbent in outdoor atmospheric condition (RH ten–60%, GHI < ane,000 W one thousand⁻²), and show inflections in performance along similar ranges as population distributions^11,31 (RH 30–50%, GHI 400–600 W m⁻²). Advancements in device efficiencies from innovative design architectures³⁸ and novel high-performance physical sorbents^{fifteen,17,39,40,41} show hope for increasing SC-AWH output. Individual specific yields from materials experiments or prototypes can be plotted in Fig. 4d for benchmarking against target impacts. Validated device performance in outdoor field weather and published output tables and are needed for global researchers to advance progress of AWH.

The long-term averaged output of an AWH device is an important but limited metric. Seasonal, weekly and diurnal variability in output will influence user adoption and market place viability. Some seasonal profiles are explored in Extended Data Figs. 4–8. Short periods of shortfall may exist supplemented by storage from previous surpluses. Rainfall collection or alternative sources would exist required for seasonal shortfall periods, such as those in monsoon climates. Use of multiple h2o sources and seasonal switching are well established in the literature, although there may be trade-offs with respect to h2o quality and contamination^42,43, reinforcing the demand for in-depth knowledge of existing h2o admission practices when deploying AWHs, with a focus on household water treatment and safe storage.

The hydro-ecological impacts of AWH for drinking water are probably negligible given the scale of the global atmospheric h2o budget. Serving all 2.2 billion people without SMDW at 10 l d^−one sums to approximately viii kmⁱⁱⁱ twelvemonth⁻¹, a mere 0.20% of the net water extraction of global cropland (4,000 km³ yr^−ane) and 0.01% of total evapo-transpiration over land⁴⁴ (65,500 km^three yr⁻¹).

SC-AWH devices have the potential to be low-cost. Most design architectures have few moving parts (for example, a slowly rotating sorbent cycle⁸), and can be constructed from widely bachelor components. Avant-garde sorbent materials (for example, MOFs or TRP) will demand to exist mass manufactured to accomplish cost targets. New high-volume manufacturing methods for MOFs^45,46 have the potential to drastically reduce costs.

Technology development is merely one office of the complex problem of safety water access; user-axial formative inquiry with a wide variety of end users is critical for ensuring that devices are adopted widely. Similar to bottled water²¹ SC_AWH devices could paradoxically undermine efforts to develop permanent piped infrastructure. Production affordability and adoption require parallel financial and socio-cultural efforts such as scaling availability of loans, promoting awareness of waterborne disease take a chance and increasing women's influence over community decisions^47,48,49.

Our analysis demonstrates that daytime climate conditions may in fact be sufficient for continuous-mode AWH operation in world regions with the highest human need. This assessment suggests that focusing device design criteria on maximum bear upon and reducing costs of off-filigree production of drinking water at the household scale is a worthwhile try.

Methods

Water access information processing

Data on drinking water coverage by region was acquired from the WHO/UNICEF JMP. The JMP acts as official custodian of global information on h2o supply, sanitation and hygiene² and assimilates information from administrative data, national demography and surveys for individual countries, and maintains a database that can be accessed online through their website. Nosotros accessed data tables for national and subnational drinking water service levels from https://washdata.org.

JMP datasets are not geographically linked to official boundary files. We joined the tables to GIS boundaries obtained from the following open-source collections: GADM (https://gadm.org), the Spatial Data Repository of the Demographic and Health Surveys Programme of USAID (DHS) and the Global Data Lab of Radboud University (GDL)^{2,50,51,52,53}. Subnational regions reported by the JMP are unstructured, representing various regional authoritative levels (province, state, district and others).

The JMP national and subnational data were joined to GIS boundaries using a custom geoprocessing tool built in Python and ArcGIS ten. The tool joins the available JMP subnational-level survey information to the closest proper name match of regional purlieus names from a merged stack of GADM (admin1, admin2 and admin3), DHS and GDL boundaries worldwide. The JMP national-level survey data is and so joined to GADM national (admin0) boundaries for countries which accept no subnational data bachelor. Finally, the 2 boundary-joined datasets (national and subnational) are merged, processed and exported equally a seamless global fabric of water-stressed-population information at the highest respective spatial resolutions bachelor (Fig. 1a).

JMP does not study the breakdown between the SMDW and basic service level inside subnational regions, and instead reports a combined category chosen 'at to the lowest degree basic' (ALB). To estimate the SMDW values in subnational regions, a simple cantankerous-multiplication was performed using the splits at the national level:

$${{\rm{SMDW}}}_{{\rm{subnational}}}=\frac{{{\rm{SMDW}}}_{{\rm{national}}}}{{{\rm{ALB}}}_{{\rm{national}}}}{\times {\rm{ALB}}}_{{\rm{subnational}}},$$

where ALB_national, ALB_subnational and SMDW_national are known values.

Validation of the cross-estimation of share of SMDW from ALB for subnational regions was conducted on a reference dataset of nationally representative household surveys that nerveless information on all criteria for SMDW⁵⁴, shown in Extended Data Fig. 2. Nosotros study regression results of R ² = 0.87 and a standard fault of 3.67, indicating a bias which over-reports SMDW share and a probable underestimate of people living without SMDW in our report. This discrepancy comes from JMP calculations of SMDW that rely on the minimum value of multiple drinking water service criteria (free from contamination, available when needed and accessible on premise) rather than considering whether individual households meet all criteria for SMDW⁵⁵.

The fraction of population without SMDW was multiplied by residential population values in the WorldPop top-downwardly unconstrained global mosaic population count of 2017 at 1 km spatial resolution⁵⁶ (https://www.worldpop.org). WorldPop was accessed online as a TIF image and imported to Google Globe Engine. The year 2017 was chosen to more closely friction match h2o access data from JMP. The percentages reported by JMP are probably not uniform within most regions⁵⁷, introducing an unknown fault to Fig. 1b, but correspond the all-time estimate available to us given the limitations of these regionally reported data.

Climate input and conversion approximations

GHI and reference plane

We used GHI (in W m⁻ⁱⁱ) as solar free energy input data. GHI has skillful availability in climate datasets and introduces the fewest number of assumptions. Since GHI describes the irradiance in a locally horizontal reference plane, this approximation is only exact for devices having a horizontally oriented solar harvesting area. Annually averaged comparisons between horizontal and optimal fixed-tilt panels prove negligible differences in direct plus lengthened radiation in tropical latitudes, and ratios below 25% in locations within fifty° north and south latitudes⁵⁸. Those seeking precise absolute predictions for tilted devices or higher latitudes are encouraged to suit the provided code to their specific assumptions.

Conversion from SY to AWH output

As discussed in the main text, solar-driven AWH devices typically take one of 2 predominant energy inputs: thermal (converted directly from incident sunlight on the device) or electrical (from PV). Here, the energy units used to calculate yield in l kWh⁻¹ are incident solar energy directly from GHI. The diverse assumptions are fabricated in relation to the reported values based on their source. The thermal limits³³, target curves, and experimental results reported by TRP¹⁵ and MOFs were causeless to accept direct (100%) conversion from sunlight to heat. For the ZMW device, the table provided by the manufacturer accounts for system losses, and then the table values were direct converted in our model³⁵. For ref. ³⁴ and the cooler–condenser limits from ref. ³², which both assume work input instead of heat, we applied a typical PV conversion efficiency of 20% to convert from sunlight kWh (GHI) to kWh_PV (electrical work) input to the device⁵⁹.

Sufficiently curt sorbent cycling times

AWH-Geo assumes continuous or quasi-continuous AWH. AWH-Geo considers each ane-h timestep independently and is thus stateless. Aside from border cases, this is a rubber assumption for mass efficient SC-AWH devices, which typically take time constants shorter than 1 h, both for sorbent cycling and for most of the thermal time constants. For devices with longer fourth dimension constants, batch devices or processes with tiresome (de)sorption kinetics, this assumption may introduce increased mistake, and may crave farther adaptation of the provided code.

Climate time-series calculation

AWH-Geo is a resource-assessment tool for AWH. It consists of a geospatial processing pipeline for mapping water production (in litres per unit of measurement time) around the earth of any solar-driven continuous AWH device that tin can exist characterized by an output table of the class output =f(RH, T, GHI).

Output tables show AWH output values in l h⁻ⁱ or l h⁻¹ m^−two across permutations of the three master climate variables in the following ranges: RH between 0 and 100 % in intervals of 10%, GHI between 0 and 1,300 Due west m⁻² in intervals of 100 Westward grand⁻², and T between 0 and 45 °C in intervals of two.v °C (2,145 full output values). The tables are converted into a 3D assortment epitome in Google World Engine and candy across the climate fourth dimension-series image collection for the flow of involvement. Finally, these AWH output values are composited (reduced) to a unmarried fourth dimension-averaged statistic of interest as an prototype.

Climate fourth dimension-series information was acquired from the ERA5-State climate reanalysis from the European Centre for Medium-Range Weather condition Forecasts (ECMWF)⁶⁰, accessed from the Google World Engine data catalogue. ERA5-Land surface variables were used in 1-h intervals and 0.ane°× 0.i° (nominal ix km). The x-twelvemonth analysis period (2010–2019, inclusive) was used for this work, and represents a period long enough to provide a reasonable correction for medium-term interannual climatic variability.

Climate variables GHI and T were matched to ERA5-Country parameters 'Surface solar radiations downwards' (converted from cumulative to mean hourly) and 'two metre temperature' (converted from K to °C), respectively. RH was calculated from the ambient and dew bespeak temperature parameters in a relationship derived from the August–Roche–Magnus approximation⁶¹ rearranged as:

$${\rm{RH}}=100 \% \times \frac{{{\rm{e}}}^{\left(\frac{a{T}_{{\rm{d}}}}{b+{T}_{{\rm{d}}}}\correct)}}{{{\rm{e}}}^{\left(\frac{{aT}}{b+T}\right)}}$$

where a is 17.625 (constant), b is 243.04 (abiding), T is the ERA5-Land parameter 'two metre temperature' converted from K to °C, and T _d is the ERA5-Land parameter '2 metre dewpoint temperature' converted from K to °C.

Spot validation of the climate parameters and the mapped output was performed manually in Google World Engine across several timesteps in 2016 in Ames, Iowa (using the Iowa Environmental Mesonet AMES-8-WSW station⁶²) and showed insignificant mistake (< v%).

Mapping upper bounds

Effigy 3a maps thermodynamic upper bound outputs for SC-AWH based on an equation from Kim et al. ³³, reproduced below.

$$\frac{{\dot{Q}}_{{\rm{hot}},{\rm{in}},{\rm{\min }}}}{{\dot{k}}_{{\rm{water}},{\rm{out}}}}=\left[\frac{i}{{\omega }_{{\rm{air}},{\rm{in}}}-{\omega }_{{\rm{air}},{\rm{out}}}}({e}_{{\rm{air}},{\rm{out}}}-{east}_{{\rm{air}},{\rm{in}}})+{due east}_{{\rm{water}},{\rm{out}}}\right]\times {\left(1-\frac{{T}_{{\rm{ambient}}}}{{T}_{{\rm{hot}}}}\right)}^{-1}$$

where ${\dot{Q}}_{{\rm{hot}},{\rm{in}},{\rm{\min }}}$ is the minimum input rut flux (in W_heat) required to drive the procedure, ${T}_{{\rm{hot}}}$ is the temperature (in Thou) at which the input oestrus is delivered, ${T}_{{\rm{ambient}}}$ is the ambient temperature (in K) at which heat is rejected and water and air exit the process, ${\dot{m}}_{{\rm{water}},{\rm{out}}}$ is the production charge per unit of liquid water past mass, $\omega $denotes humidity ratios in kg of water per kg of dry air, ${east}$ denotes specific exergies, which tin can be looked up for given temperatures and humidities, subscript air,in denotes ambient air fatigued in at ${T}_{{\rm{ambient}}}$ from which to extract moisture, subscript air,out denotes air exiting the process at ${T}_{{\rm{ambient}}}$ after extracting some moisture from information technology, subscript h2o,out denotes liquid water exiting the process at ${T}_{{\rm{ambient}}}$ equally the desired product.

Parameters not nowadays in this formula, but that are in Kim's underlying derivation: this upper limit is obtained for a small recovery ratio (RR ~ 0) chosen for numerical stability and for reversible process conditions (entropy generation,S _gen = 0).

Kim's model assumes an AWH in which the fundamental energies required are driven past input heat supplied at a temperature ${T}_{{\rm{hot}}}$. The limit information technology represents applies independent of the process, number of stages, sorbent choice, and so on, equally long as rut drives the procedure.

We adapt Kim's model to solar energy input, assuming an idealized conversion efficiency from solar irradiance to usable heat of 100%. This idealization retains a robust upper bound without bringing in additional parameters. Literature values for theoretical sunday-to-heat efficiency limits range from >99.99 to 95.80% for thermal absorbers, depending on the level of angular selectivity⁶³.

Rearranged, Kim's model yields

$$\frac{{\dot{Five}}_{{\rm{water}},{\rm{out}}}}{A}\le {Eastward}_{{\rm{GHI}}}\times \left(1-\frac{{T}_{{\rm{ambient}}}}{{T}_{{\rm{hot}}}}\right)\times {\left[\frac{1}{{\omega }_{{\rm{air}},{\rm{in}}}-{\omega }_{{\rm{air}},{\rm{out}}}}({e}_{{\rm{air}},{\rm{out}}}-{eastward}_{{\rm{air}},{\rm{in}}})+{e}_{{\rm{h2o}},{\rm{out}}}\right]}^{-1}\times \frac{1}{{\rho }_{{\rm{water}}}}$$

where, in add-on, ${\dot{V}}_{{\rm{water}},{\rm{out}}}$ is the product charge per unit of liquid water past book, ${A}$ is the area harvesting sunlight (encounter approximation department below), ${East}_{{\rm{GHI}}}$ is GHI in W_sun thousand⁻², and ${\rho }_{{\rm{water}}}$ is the density of h2o.

This is now a function of the three cardinal climate variables: GHI (in the first term), ambient temperature (in the second and hidden in the third term) and RH (entering the third term). This was converted to an output table and processed through the AWH-Geo pipeline and presented in Fig. 3a. While this can exist run for any option of parameter ${T}_{{\rm{hot}}}$, we present figures hither for ${T}_{{\rm{hot}}}$ = 100 °C, a temperature still achievable in low-cost (non-vacuum) practical devices without tracking or sunlight concentration. College driving temperatures increase the upper bound for water output. For the limits analysis, values of RH above 90% are clamped to prevent unrealistically loftier theoretical outputs as Kim'south equation goes to infinity at 100% RH. A further assumption is made that new ambient air is efficiently refreshed.

Figure 3b maps the maximum yield for active cooler–condensers without recuperation of sensible heat—all given work input and an optimum coefficient of performance of the cooling unit at a condenser temperature that maximizes specific yield every bit modelled by Peeters³², which we digitized from their fig. eleven. Peeters chose to set up yield to zero whenever frost formation would be expected on the condenser. Since Peeters assumes piece of work input, we catechumen from solar energy (GHI) to kWh_PV equally discussed above.

Effigy 3c maps Zhao's experimental results from a TRP using a logistic regression curve fit to their reported SYs of 0.21, three.71 and nine.28 l kWh⁻¹ at 30, sixty and 90% RH, respectively¹⁵. The terms of the curve fit are reported in the side by side section.

Custom yellow to blue map colours are based on www.ColorBrewer.org, past C. A. Brewer, Penn Land⁶⁴.

Specific yield and target curves

2 simple characteristic equations, linear and logistic, were used to fit a express set of SY and RH pairs from laboratory experiments or reported values and plotted through AWH-Geo using calculated output tables. Hypothetical curves of like grade whose terms were adjusted iteratively in AWH-Geo to goal-seek a target output (5 50 d⁻¹) and user base, and are reported hither (for 1-m² devices). In the following equations, RH in % is taken as a fraction (for example 55% is equivalent to 0.55).

The linear target curve is a simple linear function which crosses the y-axis at zero:

$${\rm{SY}}({\rm{RH}})=a\times {\rm{RH}}$$

where a is prepare to 1.60, 1.86 and 2.60 L/kWh to reach targets of 0.v, 1.0, and 2.0 billion people without SMDW, respectively, and RH is input RH (fractional).

The logistic target curve is a logistic function:

$${\rm{SY}}({\rm{RH}})=\frac{Fifty}{i+{{\rm{due east}}}^{-grand({\rm{RH}}-{{\rm{RH}}}_{0})}}$$

where L is ready to i.80, 2.40 and 4.eighty L kWh⁻ⁱ to reach targets of 0.5, one.0 and 2.0 billion people without SMDW, respectively, k is the growth charge per unit set to 10.0, and ${\rm{RH}}$ and ${{\rm{RH}}}_{0}$ are input RH (fractional), and 0.60, respectively.

The SY values reported by Zhao for TRPs (which they term 'SMAG') were fit to a logistic part of the same form with the following parameters: L set to 9.81 L kWh⁻¹, k set to eleven.25 and RH₀ set to 0.645.

The resulting fitted SY profile is expanded into an output table. As with all reports providing SY values instead of full output tables, this forces an assumption of linearity in oestrus rate (approximately equal to GHI), which may introduce error at lower GHI levels. Zhao reports SY of the TRP fabric is consequent across temperature below 40 °C—the fabric's lower critical solution temperature—higher up which its performance drops precipitously. Accordingly, we set the SY to 0 l kWh⁻¹ for temperatures ≥twoscore °C in the output tabular array.

Bagheri reported performance of 3 existing AWH devices across several climate conditions using an 'energy consumption rate' in kWh/L, which tin can be considered to be the SEC, and the simple reciprocal of SY. Instead of plumbing fixtures a logistic bend to the reciprocals, we fit an exponential role to the average SEC of the three devices in conditions above xx °C of the equation:

$${\rm{SEC}}({\rm{RH}})=9.03{{\rm{east}}}^{-2.99{\rm{RH}}}$$

where SEC is specific energy consumption in kWh_PV l⁻¹ and RH is fractional.

This was applied to RH and taken as reciprocal in an output table and run through AWH-Geo. Since Bagheri reports the equivalent of kWh_PV, nosotros scale to adapt to GHI input with a photovoltaic conversion efficiency as discussed in a higher place.

For performance of the ZMW device (the company'due south ~three mⁱⁱ SOURCE Hydropanel), we used values from the panel production contour plot in the technical specification sheet available from the manufacturer'due south website³⁵. The conclusion for inclusion was fabricated attributable to the importance equally an early example of a SC-AWH product with commercial intent. Values in l per panel per day were taken at each 10% RH step at 5 kWh chiliad⁻ⁱⁱ, assumed to represent kWh m⁻ⁱⁱ d⁻ⁱ, and divided by 15 kWh (~3 m² × 5 kWh m⁻²) to catechumen to SY in l kWh⁻¹. From the resulting SY curve, an output table was generated and processed with AWH-Geo.

Coincidence analysis and population sums

The coincidence analysis was run through AWH-Geo across 70 threshold pairs given the full permutation fix of RH from 10 to 100% and GHI from 400 to 700 W m⁻² threshold intervals, using binary image fourth dimension series. The resulting hateful multiplied by 24 represents average hours per twenty-four hour period thresholds are met simultaneously, giving ophd. Below is a functional representation of this time-series calculation:

$${\langle ({{\rm{RH}}}_{t,{\rm{px}}} > {{\rm{RH}}}_{{\rm{threshold}}}){{\rm{\& \& }}}_{{\rm{simultaneous}}}({{\rm{GHI}}}_{t,{\rm{px}}} > {{\rm{GHI}}}_{{\rm{threshold}}})\rangle }_{{\rm{time\; boilerplate}}}$$

where ${{\rm{RH}}}_{t,{\rm{px}}}$ is the RH in the map pixel ${\rm{px}}$ at time $t$, ${{\rm{RH}}}_{{\rm{threshold}}}$ is the threshold of RH higher up which the device is assumed to operate, ${{\rm{GHI}}}_{t,{\rm{px}}}$ is the GHI in the map pixel ${\rm{px}}$ at time $t$, and ${{\rm{GHI}}}_{{\rm{threshold}}}$ is the threshold of GHI to a higher place which the device is assumed to operate.

The population adding was then conducted on these images in Google Earth Engine.

Zonal statistics were performed on the hateful ophd images as integers (0–24) using a grouped image reduction (at ane,000-m scale) summing the population integer counts on the population without SMDW distribution image created previously (derived from WorldPop). This reduction was performed at 1,000 m. Validation was performed in Google Globe Engine on single countries within single ophd zones and showed insignificant fault (<2%). The population results were collected as a table (feature drove) and population was summed cumulatively within stacked ophd zones. These were exported to R for plotting in Fig. 4b.

To assess the sensitivity of results to the selection of climate and population dataset, we performed a coincidence analysis (Fig. 4b) with alternative datasets and provide those results in Extended Data Fig. 1.

As an alternative climate dataset to ERA-five (1 h, 9 km), we used NASA's Global State Data Assimilation System (GLDAS) 2.1 at 0.25° × 0.25° spatial resolution (nominally 30 km) and three h temporal resolution⁶⁵ during the period concurrent with the main results, 2010–2019. As an culling population dataset to WorldPop 2017, we used Oak Ridge National Laboratory'south LandScan 2017 ambience population counts at 1 km spatial resolution⁶⁶. Ii results comparisons were calculated: (ane) GLDAS calculated with WorldPop 2017 for direct comparison of climate data input, and (2) GLDAS calculated with LandScan for comparing of climate and population dataset substitution.

The intercomparisons suggest there is negligible sensitivity to the population dataset used, but substantial and systematic sensitivity to the climate dataset used, while all intercomparisons agree in primary features and qualitative conclusions. The spatially and temporally (3×) coarser GLDAS dataset consistently results in lower predictions of water output and impact than the effectively ERA-5 climate reanalysis. Nosotros speculate that the three-h timesteps of GLDAS are bereft to capture the performance-critical humidity and GHI dynamics throughout the day (probably morning and evening hours), and, similarly, the 30-km pixels are bereft to resolve fine-scale climate patterns driven by topographic and other microscale physiographic effects. This illustrates the importance of using high-resolution climate datasets.

Variability statistics of AWH output

To go beyond almanac averages and study availability, we introduce a set of metrics we named moving boilerplate density 90th percentile (MADP90).

The MADP90-t represents a device's boilerplate output rate (l d⁻¹ one thousand⁻ⁱⁱ) that will be exceeded for 90% of periods lasting t days at the given location. MADP90 is calculated from the derived P90 value beyond a probability density function (PDF) of daily mean output during each t-twenty-four hour period window in the time serial (2010−2019). The result is a scalar that can be mapped spatially. Moving-window periods of 1, 7, 30, 60, 90 and 180 days were examined in this study. MADP90-results are available as boosted results and map layers in AWH-Geo.

Extended Data Fig. 3 provides an instance set of PDFs for a location in southwest Tanzania. Each of the P90 values represent to a version of the MADP90 metric corresponding to a moving window period. The P90 value naturally increases with t in most geographic locations as the PDF tightens its dispersion about the natural (P50) mean.

Data availability

The software and datasets generated during and/or analysed during the current report are bachelor in the post-obit repositories. GitHub: https://github.com/AWH-GlobalPotential-X/AWH-Geo; Figshare: https://doi.org/10.6084/m9.figshare.c.5642992.v1; JMP Geoprocessor package (Python and ArcGIS geoprocessing model); JMP Geofabric dataset (shapefile); population without SMDW image information layer (geoTiff); upper limit AWH output data layers (geoTiff); coincidence analysis results data tables (Sheets); and output tables used in this report (Sheets). Source data are provided with this newspaper.

Lawmaking availability

The software used during the current written report is available as follows. GitHub: https://github.com/AWH-GlobalPotential-X/AWH-Geo; AWH-Geo application: processor and output viewer with source code; population and event data processing scripts.

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Acknowledgements

We acknowledge contributions from many colleagues, including A. Aron-Gilat, D. Youmans, G.L. Whiting, Chiliad. Eisaman, Southward. Lin, J. Sargent, S. McAlister, S. Chariyasatit, B. Dixon, E. St Jean Duggan, F. Carlsvi, G. Stratton, M. McCoy, R. Hessmer, J. Hanna, H. Riley, P. Watson, One thousand. Day, B. Quintanilla-Whye, A. Ramadan, A. Little and D. Moufarege. We give thanks the WHO/UNICEF JMP team for guidance on drinking water service estimates, in particular T. Slaymaker, R. Johnston and F. Mitis; the team at Google Earth Engine, in particular S. Ilyushchenko, Southward. Agarwal, T. Erickson, N. Gorelick, M. Hancher, K. Dixon, K. DeWitt, J. Conkling, N. Clinton, Yard. Reid, Due east. Engle, W. Rucklidge and the entire Earth Engine development community for communication; C. Caywood for code review; B. Schillings and J. Gagne for internal sponsorship at X. Funding was provided by Google LLC.

Author information

Affiliations

X, The Moonshot Factory, Mountain View, CA, U.s.a.

Jackson Lord, Ashley Thomas, Neil Treat, Matthew Forkin, Cyrus H. Behroozi, Tilek Mamutov, Jillia Fongheiser, Nicole Kobilansky, Shane Washburn, Claudia Truesdell, Clare Lee & Philipp H. Schmaelzle
WHO/UNICEF Joint Monitoring Programme, Division of Data, Analytics, Planning and Monitoring, UNICEF, New York, NY, USA

Robert Bain
Google Inc., Mount View, CA, USA

Pierre Dulac

Contributions

P.H.S. and J.L. conceived the study. J.L., P.D., T.M. and Due north.T. performed analysis and plots. A.T., N.T., J.L., P.H.S., R.B. and C.H.B. developed arguments. J.L., P.H.S., A.T. and R.B. wrote the newspaper. This report was conducted as a subset of a larger effort at X, led by P.H.Southward., M.F., N.T. and A.T., to develop a household AWH equally a commercial product, which informed the current report: M.F., North.T. and S.Due west. led epitome development and experimentation, C.H.B. conducted physical modelling, M.F., Southward.Westward., C.T., C.L. and others built devices and conducted experiments, A.T., J.F. and Northward.Thousand. conducted market and user research.

Respective authors

Correspondence to Jackson Lord or Philipp H. Schmaelzle.

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Competing interests

Nosotros disclose the following potential competing interests. This work was funded by X, The Moonshot Factory (formerly known equally Google[ten]). X is a part of Alphabet. Both are for-profit entities. 10 has filed for patent protection for water-from-air devices, on which multiple authors are listed equally inventors. Water-from-air devices may stand for significant commercial opportunities upon meeting sure metrics. This work may exist pursued further in various ways, including as a possible spinout visitor in which one or more authors may become founders, officers, shareholders, employees or otherwise involved with a financial interest.

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Peer review information Nature thanks Marisa Escobar and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are bachelor.

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Extended information figures and tables

Extended Data Fig. 1 Comparison of coincidence analysis results to input datasets.

Main results from coincidence analysis (Fig. 4b, people without SMDW served past opH/d of coincident climate threshold) with ERA5-Land and WorldPop 2017 datasets compared with results from (a) GLDAS ii.1 climate and WorldPop 2017 population, and (b) GLDAS 2.1 climate and LandScan 2017 population datasets. Operational hours per day (opH/d) shown across global horizontal irradiance (GHI) and relative humidity (rH) thresholds.

Extended Data Fig. ii Validation of SMDW using household surveys reporting SMDW at household-level.

(a) Charted and (b) tabulated validation of cross-estimation of percentage safely managed (SM) from at least basic (ALB) drinking h2o ladders at sub-national (SN) level from national (Due north) breakdowns using known reference data set at SN level from WHO/UNICEF JMP information. Reference values from nationally representative Multiple Indicator Cluster Surveys integrating water quality testing (ref. SM) compared with our estimates from the JMP Geoprocessor combining JMP sub-national estimates for ALB and national estimates for safely managed drinking h2o services (est. SM). Ordinary to the lowest degree squares regression (OLS) resulted in standard mistake (stdErr) equally reported. Sample size n = fifteen. Tabular array (b) shows main results (ERA5-Land) population counts later adjustment from regression. Population without safely managed drinking water (SMDW) shown across global horizontal irradiance (GHI) and relative humidity (rH) thresholds.

Extended Data Fig. 3 Visual representation of MADP90 concept from location in Tanzania.

Histograms of moving-averaged output (L/d/yard^two) beyond window periods (indicated in days) for a location in Manda, Tanzania. P90 availability value increases as averaging window catamenia increases. P90 values are estimated and for illustrative purposes only.

Extended Information Fig. four Select MADP90 metrics of AWH upper bounds.

(a) MADP90-90day, and (b) MADP90-7day values (measure of availability through time) globally for AWH thermodynamic upper bounds (Kim 2020), during ten year 2010–2019 (inclusive) analysis period.

Extended Information Fig. 5 Bi-weekly timeseries of AWH output and climate drivers for equatorial profile in Davao, Philippines.

Bi-weekly mean output (50/d/thou²), and climate inputs global horizontal irradiance (GHI, plotted from 0–1000 W/m²), relative humidity (rH, plotted from 0–100 %), and temperature (plotted from 0–100 °C) of (a) AWH thermodynamic upper bounds (Kim 2020) during ten twelvemonth 2010–2019 (inclusive) analysis period for each bi-weekly interval and (b) averaged by bi-weekly flow annually during this menstruum, and (c) for the ane billion user linear target curve for each bi-weekly interval. Example of a steady, low variability output profile characteristic of equatorial tropics.

Extended Data Fig. half dozen Bi-weekly timeseries of AWH output and climate drivers for tropical savanna profile in Accra, Republic of ghana.

Bi-weekly mean output (50/d/m²), and climate inputs global horizontal irradiance (GHI, plotted from 0–1000 West/1000²), relative humidity (rH, plotted from 0–100 %), and temperature (plotted from 0–100 °C) of (a) AWH thermodynamic upper bounds (Kim 2020) during x year 2010–2019 (inclusive) analysis menstruum for each bi-weekly interval and (b) averaged by bi-weekly period annually during this menses, and (c) for the ane billion user linear target curve for each bi-weekly interval. Example of a seasonal moisture-dry tropical savanna climate with moderate semi-annual fluctuations of AWH output driven by rH.

Extended Information Fig. seven Bi-weekly timeseries of AWH output and climate drivers for tropical savanna contour in Dhaka, Bangladesh.

Bi-weekly hateful output (50/d/m^two), and climate inputs global horizontal irradiance (GHI, plotted from 0–chiliad W/m^two), relative humidity (rH, plotted from 0–100 %), and temperature (plotted from 0–100 °C) of (a) AWH thermodynamic upper bounds (Kim 2020) during ten twelvemonth 2010–2019 (inclusive) analysis period for each bi-weekly interval and (b) averaged by bi-weekly period annually during this menses, and (c) for the 1 billion user linear target curve for each bi-weekly interval. Instance of a seasonal wet-dry tropical savanna climate with pronounced semi-almanac fluctuations of AWH output driven by rH.

Extended Data Fig. eight Bi-weekly timeseries of AWH output and climate drivers for mid-latitude profile in Ulaanbaatar, Mongolia.

Bi-weekly mean output (L/d/chiliad²), and climate inputs global horizontal irradiance (GHI, plotted from 0–1000 W/1000^two), relative humidity (rH, plotted from 0–100 %), and temperature (plotted from 0–100 °C) of (a) AWH thermodynamic upper premises (Kim 2020) during ten year 2010–2019 (inclusive) analysis period for each bi-weekly interval and (b) averaged past bi-weekly period annually during this period, and (c) for the i billion user linear target curve for each bi-weekly interval. Example of a mid-latitude climate with pronounced semi-almanac fluctuations of AWH output driven by temperature.

Extended Data Fig. nine Decadal anomaly of AWH output with logistic SY profile between 2000–2009 and 2010–2019.

(a) Overall mean output (L/d/grand^two) of 1 billion user target logistic curve at v 50/d/one thousand² during x year 2010–2019 (inclusive) period. (b) Ratio (%) anomaly of output of aforementioned specific yield (SY, in L/kWh) contour averaged over ten year 2000–2009 (inclusive) period. Red colors indicate increasing AWH output with fourth dimension between the two decades. Blue colors point decreasing AWH output.

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Lord, J., Thomas, A., Treat, North. et al. Global potential for harvesting drinking water from air using solar free energy. Nature 598, 611–617 (2021). https://doi.org/10.1038/s41586-021-03900-w

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Received: 05 February 2021
Accustomed: 11 August 2021
Published: 27 October 2021
Effect Appointment: 28 Oct 2021
DOI : https://doi.org/10.1038/s41586-021-03900-w

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