Global potential for harvesting drinking water from air using solar energy

Abstract

Access to safely managed drinking water (SMDW) remains a global challenge, and affects 2.2 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^3-6, but low specific yields (SY) and low daytime relative humidity (RH) have raised questions about their performance (in litres of water output per day)^7-11. However, 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. Here we show that AWH could provide SMDW for a billion people. Our assessment-using Google Earth Engine¹³-introduces a hypothetical 1-metre-square device with a SY profile of 0.2 to 2.5 litres per kilowatt-hour (0.1 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 average daily drinking water requirement of 5 litres per day per person¹⁴. We plot the impact potential of existing devices and new sorbent classes, which suggests that these targets could be met with continued technological development, and well within thermodynamic limits. Indeed, these performance targets have been achieved experimentally in demonstrations of sorbent materials^15-17. Our tools can inform design trade-offs for atmospheric water harvesting devices that maximize global impact, alongside ongoing efforts to meet Sustainable Development Goals (SDGs) with existing technologies.

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

a, Percentage share of total population in survey region living without SMDW as reported by the WHO/UNICEF JMP. b, Log population density of people without SMDW from WorldPop at 1 km resolution adjusted by JMP proportions at 1 km resolution. Produced in ArcGIS 10.

Fig. 2. Data processing workflow of AWH-Geo.

Cylinders indicate data stores from Google Earth Engine, the WHO/UNICEF JMP or open online content. Shown are processes (rectangles), geo-images (parallelograms) and outputs (circles).

Fig. 3. Upper bounds water output of solar-driven AWH in relation to global user base.

a–c, Mean daily water output of solar-driven AWH given overall thermodynamic limits of any process (T_hot = 100 °C) (a), cooler–condenser processes driven by PV (b) and example of active sorbent device types (TRP gels from ref. ) (c). Callout charts in a show select seasonal profiles in bi-weekly intervals of mean output and primary climate drivers: GHI, RH and temperature. Output (in l d⁻¹ m⁻²) normalized to horizontal device area in sunlight. Real devices will perform below maximum theoretical potentials. Overlaid dot density (red) of 2.2 billion people without SMDW. Placement of dots is spatially arbitrary across the survey region. Produced in ArcGIS 10. Source data

Fig. 4. AWH technology parameters in relation to global impact targets.

a, b, Geographic distribution (a) and sum (b) of population without SMDW living in areas meeting parametric thresholds relevant to operation of SC-AWH devices. Operational hours per day (Ophd) is the mean daily duration of both sunlight (GHI) and RH thresholds exceeded simultaneously. Usage example: a device requiring more than 5 h d⁻¹ of sunlight above 400 W m⁻² must operate down to 40% RH to reach approximately 700 million users. c, d, People without SMDW reachable in relation to mean daily output normalized to horizontal device area in sunlight (c) and SY profile (d). Target curves are hypothetical SY profiles capable of providing 5 l d⁻¹ for a given solar collection area. Water output and SY targets scale linearly with device area in sunlight. For demonstration we therefore show that, for a given RH, doubling the area of a device from 1 m² to 2 m² halves the target SY requirement to achieve SMDW for a target population. ZMW Source profile approximated from the manufacturer’s technical specifications sheet. Note that the full ZMW panel is approximately 3 m². Experimental values for MOFs and sorbents are taken from experiments^, (0.19 l kWh⁻¹ and 0.84 l kWh⁻¹), and TRP is taken from ref. , all converted as in ref. . Values for the Bagheri device assume work instead of heat input; therefore photovoltaic efficiencies were applied when converting from GHI. Maps are produced in ArcGIS 10. Source data

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

Main results from coincidence analysis (Fig. 4b, people without SMDW served by opH/d of coincident climate threshold) with ERA5-Land and WorldPop 2017 datasets compared with results from (a) GLDAS 2.1 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. 2. 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 water ladders at sub-national (SN) level from national (N) breakdowns using known reference data set at SN level from WHO/UNICEF JMP data. 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 water services (est. SM). Ordinary least squares regression (OLS) resulted in standard error (stdErr) as reported. Sample size n = 15. Table (b) shows main results (ERA5-Land) population counts after 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/m²) across window periods (indicated in days) for a location in Manda, Tanzania. P90 availability value increases as averaging window period increases. P90 values are estimated and for illustrative purposes only.

Extended Data Fig. 4. 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 Data Fig. 5. Bi-weekly timeseries of AWH output and climate drivers for equatorial profile in Davao, Philippines.

Bi-weekly mean output (L/d/m²), 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 year 2010–2019 (inclusive) analysis period for each bi-weekly interval and (b) averaged by bi-weekly period annually during this period, and (c) for the 1 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. 6. Bi-weekly timeseries of AWH output and climate drivers for tropical savanna profile in Accra, Ghana.

Bi-weekly mean output (L/d/m²), 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 year 2010–2019 (inclusive) analysis period for each bi-weekly interval and (b) averaged by bi-weekly period annually during this period, and (c) for the 1 billion user linear target curve for each bi-weekly interval. Example of a seasonal wet-dry tropical savanna climate with moderate semi-annual fluctuations of AWH output driven by rH.

Extended Data Fig. 7. Bi-weekly timeseries of AWH output and climate drivers for tropical savanna profile in Dhaka, Bangladesh.

Bi-weekly mean output (L/d/m²), 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 year 2010–2019 (inclusive) analysis period for each bi-weekly interval and (b) averaged by bi-weekly period annually during this period, and (c) for the 1 billion user linear target curve for each bi-weekly interval. Example of a seasonal wet-dry tropical savanna climate with pronounced semi-annual fluctuations of AWH output driven by rH.

Extended Data Fig. 8. Bi-weekly timeseries of AWH output and climate drivers for mid-latitude profile in Ulaanbaatar, Mongolia.

Bi-weekly mean output (L/d/m²), 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 year 2010–2019 (inclusive) analysis period for each bi-weekly interval and (b) averaged by bi-weekly period annually during this period, and (c) for the 1 billion user linear target curve for each bi-weekly interval. Example of a mid-latitude climate with pronounced semi-annual fluctuations of AWH output driven by temperature.

Extended Data Fig. 9. Decadal anomaly of AWH output with logistic SY profile between 2000–2009 and 2010–2019.

(a) Overall mean output (L/d/m²) of 1 billion user target logistic curve at 5 L/d/m² during ten year 2010–2019 (inclusive) period. (b) Ratio (%) anomaly of output of same specific yield (SY, in L/kWh) profile averaged over ten year 2000–2009 (inclusive) period. Red colors indicate increasing AWH output with time between the two decades. Blue colors indicate decreasing AWH output.