Assessing global drinking water potential from electricity-free solar water evaporation device

Domain: earth_science
Published: 2024-08-08
Editorial outcome: major_revision

Abstract
Universal and equitable access to affordable safely managed drinking water (SMDW) is a significant challenge and is highlighted by the United Nations’ Sustainable Development Goals-6.1. However, SMDW coverage by 2030 is estimated to reach only 81% of the global population. Solar water evaporation (SWE) represents one potential method to ensure decentralized water purification, but its potential for addressing the global SMDW challenge remains unclear. We use a condensation-enhanced strategy and develop a physics-guided machine learning model for assessing the global potential of SWE technology to meet SMDW demand for unserved populations without external electricity input. We find that a condensation-enhanced SWE device (1 m2) can supply enough drinking water (2.5 L day−1) to 95.8% of the population lacking SMDW. SWE can help fulfill universal SMDW coverage by 2030 with an annual cost of 10.4 billion U.S. dollars, saving 66.7% of the current investment and fulfilling the SDG-6.1 goal.

Introduction
Safely managed drinking water (SMDW) is an urgent requirement for people worldwide and has been included in the Sustainable Development Goals (SDG) 6.1 framework1,2. For better drinking water accessibility, global populations tend to live close to surface freshwater sources. Even in arid areas that suffer from limited surface water bodies, the median population distance to surface water is 4.3 km for Northern Africa and 4.8 km for the Middle East3. Meanwhile, ~95% of the population without SMDW live in areas with over 20 cm of annual precipitation (Supplementary Fig. 1). Surface water, groundwater, and rainwater collectively comprise the water source for the population without SMDW3,4. However, over 2 billion people still suffer from unsafe drinking water by 2015, which mainly arises from limited water treatment and poor water management. This situation has become particularly acute for populations in remote areas, who are concurrently threatened by unmanaged water sources, poverty, underdeveloped purification technology, and isolated population distribution. Traditional routes of centralized water treatment to ensure SMDW are energy- and capital-intensive and rely on the scale advantage to minimize the treatment cost. These features demand large investment and aggravate the inequality of the global water supply5,6, challenging the fulfillment of goals outlined in the SDG–6.1 framework7,8. The United Nations-estimated SMDW coverage by the year 2030 is only 81% of the global population, making the prospect of achieving 100% coverage of SMDW bleak9. Point-of-use water treatment techniques capable of providing SMDW are highly desired, especially in remote areas, with minimized upfront capital investment and reduced adverse environmental impact.

Solar water evaporation (SWE) converts solar energy to heat to initiate water evaporation to purify water from different sources to supply SMDW. Salts, heavy metal ions, organics, and pathogenic microorganisms could be removed from the water. The SWE technology is flexible, feasible, cost- and energy efficient, and has an near-zero carbon footprint, which is believed to satisfy SMDW demand in remote areas10–12. Solar evaporators, encompassing 0 Dimension (0D)/1D suspended evaporators (e.g., metal nanoparticles) to 2D interfacial evaporation film (e.g., carbon cloth) and then to 3D evaporators with larger surface areas (e.g., umbrella and tree-shaped designs) have been proposed with a solar-to-vapor efficiency of over 90% under natural irradiance (~1 kW m−2 h−1) across different water body types (sewage, seawater, brackish water, etc.)13,14.

However, a gap still exists between the evaporated vapor and the collected SMDW due to mismatches between rapid evaporation and weak condensation15–18. Important developments, including larger condensing areas, condensing materials with higher thermal conductivity, forced condensing, and multi-stage devices with latent heat recovery or driven by additional photovoltaics, have been proposed to further SMDW output even with solar-to-water efficiency over 100%15,19–22. However, the water production cost of SWE is decided by the efficiency, cost and lifetime simultaneously. Advanced solar evaporators and condensing surfaces tend to increase the cost of raw materials. Forced condensation requires additional electricity input, which weaken the SWE inherent merits of low-cost, flexible implementation and hampers its feasibility to the point-of-use water supply, especially in remote areas23. Meanwhile, previous reports relied primarily on laboratory-scale testing supplemented by outdoor operations under favorable weather conditions, and comparing the actual SMDW production performance of different SWE devices in natural conditions is necessary for its objective evaluation24. A geospatial tool (AWH-Geo) has been proposed to combine the material water yield kinetics of previous reports with dominant environmental variables to assess the global potential for harvesting drinking water from the air given available climatic resources25. It pinpoints the maximum impacts of atmospheric water harvesting to address water scarcity on a global scale, proving a great paradigm for evaluating a technique’s contribution to the SDGs. Therefore, it is important to reconsider the SWE technique for better supplying SMDW to serve SDG–6.1. Differently, considering that low-cost and flexible implementation make SWE unique, cost and efficiency should be considered to evaluate the feasibility of SWE under natural conditions. An effective tool that could anticipate the technical and economic potential of SWE and, in turn, reveal the technical bottlenecks to guide the SWE device design, is useful.

Here, we propose a physics-guided machine learning model that integrates the physical model and random forest (RF) method. It can simultaneously unveil the mass‒energy transfer mechanisms in SWE devices and assess the SMDW yield potential of the SWE technique both technically and economically. The physical model is based on pilot experiments under natural environmental conditions to clarify the principle of designing the SWE devices and establish the causality between SWE devices and meteorological parameters. With this causality, the physical model is abstracted to the RF method to simplify the calculation. The cost evaluation is also included and is merged with the SMDW yields of SWE and the population without SMDW to inspect the feasibility of SWE. The results show that the SWE proves to be suitable for extending SMDW services globally, as the SWE technique can produce enough clean water, particularly in remote areas that are facing SMDW risks. Enhancing the condensation process in SWE devices can greatly promote SMDW production in different regions worldwide. The total cost can be conserved and comprises only 1/3 of the current investment, which is promising for promoting worldwide SMDW service at the household level both geographically and technically, especially in developing areas. This is of great importance for relieving the cost and time pressure of fulfilling SDG-6.1 goals by 2030.

Results
Geography of global water scarcity
To assess the potential impact of the SWE technique, the raster gross national income (GNI) data and the people without SMDW service were first mapped together (Fig. 1). The seamless fabric of both national and subnational survey regions describes the global distribution of the GNI per capita. By coincidence, the population without SMDW service was mainly distributed in low- and lower-middle-income countries (classified based on the GNI per capita, see methods for more details, Supplementary Fig. 2). Over 1783 million people without SMDW service are in these countries, which comprise almost 81.2% of the entire global population facing unsafe drinking water supply. Meanwhile, the population density without SMDW service sharply decreases from approximately 66 to 0.26 km−2 from high-income countries to lower-middle-income countries. Therefore, economic development serves as a critical stressor contributing to water insecurity and inequity, and economic development inevitably challenges the traditional centralized water supply techniques. The cost of meeting the 2030 SDG-6.1 goals on safe drinking water was estimated to be 60.1–89.0 billion U.S. dollars ($) per year, which is almost 3 times the current investment intensity, which makes the cost unaffordable and unsustainable for most areas facing unsafe drinking water supply26. Therefore, developing a cost-effective way to provide SMDW service suitable especially for rural areas is an urgent need27.

Successive outdoor solar water evaporation for the SMDW
The SWE technique employs three steps to produce purified water, including solar-thermal conversion (solar to heat), vaporization (heat to vapor), and condensation (vapor to water, Fig. 2a). After decades of efforts, the solar-to-heat efficiency has been elevated to over 90%, and the converted heat could then initiate highly efficient vaporization13,14. However, to fulfill the solar-to-water process, condensation is also a critical point that determines the overall SMDW yield28–31. Simultaneously evaluating different strategies of condensation and evaporation could help cost-effectively promote global potential estimation of SWE for SMDW services10,16,32. We set five cases to differentiate the keys for SWE operation (Supplementary Fig. 3). As shown in Fig. 2b, case 1 is a reference system without solar evaporators and relies on bulk-heating evaporation to produce water. In contrast, solar evaporators are included in case 2. With evaporators, case 3 further pumps vapor out through a condensing tube for forced condensation with additional photovoltaics, while case 4 uses coated glass (condensation-enhanced) to condense the water with out external energy input. Case 5 integrates both the condensing tube and condensation-enhanced glass for condensation.

The results show the SWE device could purify water and SMDW yield is affected by both the device setup and the daily meteorological conditions (Fig. 2c and Supplementary Figs. 4, 5). The daytime average inner vapor concentration and temperature of the case 1 SWE device are 102.8 (62.9–137.4) g m−3 and 41.8 (30.8–56.0) °C, respectively, higher than those of the natural environment (39.2 g m−3 and 34.6 °C, Supplementary Fig. 6). The solar evaporator (case 2) SWE device further elevates the inner vapor concentration and temperature to 146.3 (71.1–211.8) g m−3 and 47.9 (31.2–62.1) °C, oversaturating the glass surface more and condensing 50.2% more water compared to the case 1 SWE device. Therefore, converting more sunlight to heat in the closed system is a critical step for creating oversaturation and producing condensing water.

Generally, condensation in the closed SWE device mainly originates from the oversaturation of the water vapor on the condensing glass cooled by the ambient air. The results show that although more solar energy is utilized to power the evaporation, the energy efficiency of the case 2 SWE device ranges from 1.1% to 47.1%, just 50.2% higher than that of the case 1 SWE device (0.0%–43.2%, Fig. 2d). Comparatively, with the condensation-enhanced process, the water yields of the case 3–5 SWE devices increase more dramatically. Interestingly, the case 3–5 SWE devices show lower average inner temperatures and vapor concentrations than the case 2 SWE device, but they show higher SMDW yields. Among them, the case 4 SWE device exhibits a greater efficiency of 13.1–84.0%, which is 99.4% and 199.5% higher than those of case 2 and case 1 SWE devices, respectively. The energy efficiency of case 4 shows no relation (Supplementary Fig. 7a, c and Supplementary Table 1, p > 0.05, not significant) to the day within the 100-day successive SMDW production test. Instead, the energy efficiency shows significant positive relations (Supplementary Fig. 7b, c and Supplementary Table 1, p < 0.001) to the solar irradiance33,34, demonstrating that the case 4 device operates stably with almost no deterioration. Moreover, the daytime average inner vapor concentration and temperature of the case 4 SWE device are only 137.8 (73.1–198.0) g m−3 and 47.3 (33.5–60.2) °C, respectively (Supplementary Fig. 6c, d). This is because the coated glass could maintain dropwise condensation, providing self-regenerated condensing sites for the saturated water vapor, and maintain effective latent heat release on the coated glass with the external environment35,36. Comparatively, pumping out the vapor through the condensing tube elevates the SMDW yield of cases 3 and 5, but their energy efficiency only ranges from 0.6–14.3% and 1.6–15.2% by taking the solar energy used for the electricity consumption of the vapor pump into consideration, respectively, which is even inferior to the case 2 device without enhanced condensation. The lower inner headspace vapor concentration and temperature of case 3 (132.1 g m−3, 46.0 °C) and case 5 (125.6 g m−3, 44.2 °C) SWE devices indicate heat and vapor inevitably leak during pumping of the inner air and affect their SMDW yield efficiency. Therefore, high temperature and vapor concentration are necessary for oversaturation but are not the determinant factors for producing SMDW services. Condensation is the bottleneck, and how to improve it dominates the SMDW yield more profoundly.

Therefore, enhanced condensation with the solar evaporator can increase the solar-to-vapor efficiency of the SWE device, and it is even more effective than the application of solar evaporators23. This is further demonstrated by performing redundancy analysis (RDA). We set the temperature, absolute humidity, wind speed, and downward shortwave irradiation (DSW) as the explanatory variables and the SMDW daily yields of cases 1–5 as the response variables (Fig. 2e). The results show that wind speed shows little relation to the solar SMDW yields (red arrows), while absolute humidity only exhibits a slightly positive relation with SMDW yields. Compared to absolute humidity, the angles between the temperature and SMDW yields decrease, demonstrating a stronger positive influence of temperature. This is due to that SWE devices could interact with natural conditions by heat exchange, which determines the condensation inside the device and the heat loss from the device to the environment. DSW poses the most dominant influence on the SMDW yields with its correlation coefficient with RDA1 of 0.99. Moreover, through optimizing the condensation, the case 3–5 SWE devices show more strongly positive relations to the DSW compared to the case 1 and 2 SWE devices, corresponding to their higher SMDW yields (Fig. 2c). Amony them, case 4 tops the positive correlation with DSW, which agrees well with its best solar energy utilization efficiency of case 4 (Fig. 2d), proving condensation could make the SWE device better utilize solar energy to produce SMDW.

Physics-guided machine learning for interpretation and prediction of the SMDW yield
To date, data-driven prediction methods have been adopted to establish the relationships between natural and social parameters, but the data-driven prediction methods showed limited capacities to pinpoint the underlying mechanisms25,37,38. The 100-day successive outdoor test provided an essential dataset to evaluate the practical solar-to-water efficiency of the SWE device24. Based on this dataset, finite element physical models were set up based on the outdoor test data to uncover the energy and mass transfer conversion in the gray box of the SWE devices and to establish the relation between critical climate parameters and SMDW yield. Moreover, this physical model could simultaneously train the machine learning model for better prediction. Therefore, we proposed a physics-guided machine learning (PGML) model to integrate both the interpretation and the anticipation of the practical SMDW yields (Supplementary Fig. 8, see “methods”)38,39.

We take the case 2 SWE device as an example of the only evaporation-optimized case (Eva. opt.) and the case 4 SWE device as the evaporation-condensation-optimized case (Eva.-cond. opt.). Hourly downward shortwave irradiance and ambient air temperature are input to construct the working conditions, and the corresponding SMDW yield is applied to optimize the model. As shown in Fig. 3a, a higher temperature simultaneously accelerates evaporation, enabling a higher vapor concentration and creating oversaturation at the vapor-glass interface. However, even the daytime temperature of the solar evaporator’s surface and vapor concentration in the Eva. opt. model is 1.7 °C and 51.0% higher than that of the Eva.-cond. opt. model, but the evaporation rate is 52.1% less and almost halves the corresponding SMDW yield (Fig. 3b). This agrees well with the pilot study results (Supplementary Fig. 6), demonstrating that condensation is the bottleneck for producing SMDW. Generated vapor is trapped in the closed Eva. opt. model due to limited condensation, which conversely hinder the successive evaporation of the solar evaporator. Therefore, optimizing condensation is the determinate factor of the SWE device, dominating both the solar-to-vapor and the solar-to-water efficiencies. Moreover, the Eva. opt. the model can sensitively react to meteorological changes, and the simulated SMDW yield is well fitted to the experimental results with a correlation coefficient (r) of 0.81 (Fig. 3c). Moreover, when inputting hourly air temperature and surface solar irradiation, the Eva. opt. the model could predict the daily SMDW yield (Fig. 3d), and the correlation coefficient is 0.83. Meanwhile, the accumulated predicted SMDW yield shows a linear correlation with the experimental results with a slope of 0.975 (~1, adjusted R2 is 0.999), demonstrating the robustness of the Eva. opt. model (Fig. 3e). Similarly, the Eva.-cond. opt. the model also well fitted the observed daily SMDW yield with r~0.83 (Fig. 3f). Its prediction of both the daily and accumulated SMDW yields shows an r~0.94 and a slope of 0.996 (adjusted R2 is 0.999), respectively (Fig. 3g, h). Therefore, based on accurately interpreting and comprehensively quantifying the energy and mass transfer processes in the SWE device, the Eva. opt. and Eva.-cond. opt. models establish a causal relationship between the weather parameters and the corresponding SMDW yield and could be generalized for estimating the global SMDW yields of SWE devices.

Based on the SWE finite element physical model, we chose 30 cities that covered the major typical population without SMDW in all continents throughout vast longitude/latitude ranges and applied the meteorological time series to predict the corresponding SMDW yields (Supplementary Figs. 8, 9). According to the importance of every meteorological input, we trained the tenfold RF models by using both today’s and yesterday’s DSW (kWh m−2) and temperature (K) in these 30 cities as the model predictors and the physical models simulated the daily SMDW yields as the training targets (Supplementary Figs. 8, 10). Then, the potential of the SWE devices to provide the SMDW service globally could be estimated by applying the global meteorological records to the RF models (Supplementary Fig. 10). The RF models showed predicting R2 values of 0.97 ± 0.0068 and 0.99 ± 0.0071 for the Eva. opt. and Eva.-cond. opt. models, respectively, and the root-mean-square errors (RMSEs) are 0.22 and 0.27 L m−2 d−1, demonstrating that the RF models have the potential to predict the global SMDW yield.

Assessing the global SMDW yield potential using a physics-guided machine learning model
To evaluate the technical feasibility of SWE, we first mapped the upper limit of the average annual SMDW yield (L m−2 day−1) under the hypothesis the DSW could be used for evaporation at 293.15 K, and all generated vapor could be condensed and collected (Fig. 4a)10,14. The estimation of this limit is significant for understanding the maximum potential SMDW yield across the world and disclosing the gap between the state-of-the-art and the ideal. As the theoretical yield of SMDW is solely dependent on the abundance of DSW, its geographic pattern closely follows the average annual DSW distribution (Supplementary Fig. 11). The SWE exhibits great potential in globally supplying SMDW services, especially in tropical areas. For areas with large populations without SMDW services, the seasonal variation in the SMDW yield demonstrated the universal feasibility of the SWE in providing local people with almost 10–12 L m−2 day−1 in the summer (Fig. 4a). In contrast, the SMDW yield in the winter varies dramatically with latitude due to the synchronous reduction in the DSW and the temperature, with only ~2 L m−2 day−1 for high-latitude cities and over 4 L m−2 day−1 for tropical and subtropical cities. Therefore, the long-term daily SMDW yield is more suitable for evaluating the availability of the SWE technique than the short-term SMDW yield under favorable conditions.

Then, the annual SMDW yields of two typical electricity-free scenarios (the Eva. opt. and Eva.-cond. opt. models) were mapped. For the Eva. opt. models, even the third quartile of the annual average SMDW yield is only 1.76 L m−2 day−1, and the SMDW yields in most countries are below this value (Fig. 4b and Supplementary Fig. 12), which fails to satisfy the daily water intake defined by the World Health Organization (2.5 L per capita per day)40,41. In contrast, the global median SMDW yield is elevated to 3.77 L m−2 day−1 for the Eva.-cond. opt. model, with the yield in most countries exceeding this value (Fig. 4c and Supplementary Fig. 12). The third quartile of the country-level SMDW yields is 4.27 L m−2 day−1, more than 2-fold that of the Eva. opt. model (1.76 L m−2 day−1). As excessively low temperatures pose an adverse influence, the SMDW yields of both scenarios exhibit dramatic seasonal variations. The Eva.-cond. opt. model (up to over 7 L m−2 day−1) outperforms the Eva. opt. model by more than 3 times (Supplementary Fig. 13), especially in summer. The solar-to-vapor efficiency of the Eva.-cond. opt. the model reaches ~60% of the upper limit. However, the SMDW yield in the winter decreases dramatically, and the gap between the Eva.-cond. opt. model and the Eva. opt. model decreases. The SMDW yields of both models are far less than the upper limit, as the upper limit estimation leaves out the adverse influence of low temperature, which contributes greatly to its high average SMDW yield. Therefore, this also informs ongoing efforts that could still lead to better SMDW yield within the thermodynamic limit.

Extending SMDW service and advancing SDG-6.1 by SWE
Going beyond technical feasibility, the specific cost of implementing SWE to supply SMDW is also crucial for extending SMDW service. As the population distributed without SMDW service in less-developed areas is harder to satisfy and relies on local government effort and global cooperation, the SMDW yield was further classified and analyzed according to the income levels of different countries. The population without SMDW services and SMDW yield were merged with 0.1 million and 2.5 L m−2 day−1 as the demarcation point of the quadrant (Fig. 5a). For Eva.-cond. opt. model, the high-income countries (classifications are included in the methods–cost evaluation of SWE) are mainly distributed in the second and third quadrants, indicating their low demand for SWE application (Fig. 5a and Supplementary Fig. 14). In comparison, the remaining underdeveloped countries facing SMDW risks almost fall within the first quadrant, exactly matching the SWE resources. Therefore, condensation-enhanced SWE is extremely suitable for underdeveloped areas and it could provide these critical portions of the population lacking SMDW service with over 2.5 L m−2 day−1 annually, securing individual daily drinking water within 1 m2 of the working area. However, this is impossible for the Eva. opt. the system, as its SMDW supply potential is limited and shows little variance across countries of different income levels (Supplementary Figs. 15, 16). Further reaching the upper limit of SWE, some high-income countries could ascend out of the third and fourth quadrants, but SWE is not urgently needed there (Supplementary Figs. 17, 18).

Fig. 5b shows the output service coverage of different systems normalized by the SMDW yield. For the Eva.-cond. opt. model, 95.8% of the population without SMDW is covered by climates favorable for SWE use, where 1 m2 could sustain at least 1 person’s daily drinking water. This is superior to the Eva. opt. model, which only covers 2.8% of the total population. When doubling the working area of the Eva-cond. opt. model to 2 m2, its performance could close the gap and even surpass the upper limit of the SWE with a working area of 1 m2. Moreover, by finely recovering the heat of the generated vapor during condensation, a multi-stage design may further elevate the SMDW yield, enabling reinforcement of SMDW services in underdeveloped countries. However, this inevitably leads to extra complexity and cost, which dampens the affordability and accessibility of SWE techniques42.

To achieve SDG-6.1 goals, the World Bank has estimated the total cost needed to promote SMDW coverage26. Ideally, an average annual cost of $80.3 billion (range: 60.1 to $89.0 billion) is needed to extend SMDW services to the unserved population (the cyan dashed line and bars shown in Fig. 5c). However, the actual investment is only 1/3 of the ideal, and this large gap lead to severe delays in the development of further SMDW coverage26. Until 2020, the coverage is only 74%, far less than the expected coverage of 81.6% (blue dashed line and bars). Under the current investment trend, the coverage expected by 2030 would be 81%, which fails to meet SDG-6.1 goals9. According to the methods of the World Bank, we mapped the capital expense (without software) globally (Supplementary Fig. 19). Then, the average annual costs (capital, maintenance, and operation costs) of the SWE approach to close the gap between current and ideal investment (see “methods”) are estimated at $26.1 and $10.4 billion for Eva. opt. and Eva.-cond. opt. models, respectively. Benefiting from the cost-effective design of the coated top cover glass for sustainable condensing, no external energy input and auxiliary facilities are needed for the Eva.-cond. opt. model and its cost only comprise 33.3% of the current investment trend ($29.4 billion), while the cost of the Eva. opt. the model shows little advantage (Fig. 5c). Therefore, the SWE technique is promising and constructive in satisfying the remaining SMDW service-lacking population without changing the current investment trend, which offers a reasonable alternative option to address the impossibility of closing the financing gap (~$800 billion), especially under the destructive burden of COVID-19 on the global economy.

Methods
Setup of solar evaporation devices
The solar evaporation device was designed with a cuboid-shaped container and a wedge-shaped top cover (Supplementary Fig. 20). The container consisted of an acrylic water tank and a glass top cover. The projection area of the devices is 17 × 17 cm2. The top of the backboard is 29 cm in height and the inclination of the glass top cover is ~22.3°. All device types were installed on the roof of one building in Beijing, China (Supplementary Fig. 3). The whole device was well-sealed and insulated from the ground to avoid direct ground heating. In the devices, the temperature and humidity sensors (TH10S-B, MIAOXIN) were fixed at the top of the backboard, which was connected to an Internet of Things (USR-G781-43, Jinan USR IOT Technology Limited) for data recording. A water storage tank (35 × 35 × 45 cm) was used to distribute the raw water to the above 5 devices by gravity, and the water level in each container was controlled by a level control valve to 16.0 cm. The raw water was made of artificial brackish water composed of NaCl (1.0006 g L−1), CaCl2 (0.2775 g L−1), Na2SO4 (0.4925 g L−1), and MgCl2·6H2O (0.4177 g L−1). Diversion trenches were fixed on the four walls of the container to collect the condensed water. The condensed water then got out of the devices through a single silicone tube or a condensing tube immersed in the water, which varied with the 5 device types in our pilot study (Fig. 2b).

Case 1 was a reference system without solar evaporators. Sunlight is directly irradiated into the bulk water to accelerate evaporation and the generated vapor is then condensed on the glass top cover to produce water. Case 2 included solar evaporators to float on the bulk water compared to case 1. The solar evaporators were fabricated via the pyrolysis of the sugarcane, as reported before, and they could utilize ~97% of solar energy44. Case 3 included solar evaporators and further pumped the headspace vapor through a condensing tube immersed in the bulk water for forced condensation with additional photovoltaics and a vapor pump. The condensed water on the glass top cover and condensing tube comprised the produced water. Case 4 included solar evaporators and used a coated glass top cover (condensation-enhanced) to condense the vapor without external energy input. This coating could ensure the condensed droplets are quickly shed from the glass top cover, so the condensing active sites could be regenerated for enhanced condensation. The fabrication method for the coating layer is included in Supplementary Note 136. Case 5 included solar evaporators and integrated both the condensing tube immersed in the bulk water and the coated glass top cover for condensation. The headspace vapor was pumped through the condensing tube immersed in the bulk water for forced condensation with additional photovoltaics and a vapor pump. The condensed water on the coated glass top cover and condensing tube comprised the produced water.

The daily water output was calculated as Eq. (1):

SMDW yield (kg m−2 day−1) = mass of daily collected water (kg) / evaporation area (m2) (1)

Where the mass of the daily collected water is measured by a graduated cylinder at 18:00 every day. The conductivity of the produced water from each device was tested by a conductivity meter (S230, METTLER). The ion concentration was determined by ion chromatography (Aquion, Thermo Fisher).

The SMDW yield and energy efficiency calculations
The solar-to-water energy efficiency was calculated as the ratio between the heat of the generated vapor and the consumed energy45 as Eq. (2):

η solar-water = (m_ × hfg) / (q solar + q solar-electricity) (2)

Where, m_ is mass flow of the SMDW yield (kg m−2), hfg is the latent heat (kJ kg−1), q solar is the incident solar intensity for heat conversion (kJ m−2) and q solar-electricity is the incident solar intensity for electricity generation (kJ m−2). The power of the vapor pump is 5 W, and it works all day to pump the headspace water vapor in a work area of 17 × 17 cm2. Considering that the photovoltaic cells available in the market have an average energy conversion efficiency of 20%46, the incident sunlight was estimated as electricity consumption / 20%.

Spatial distribution of population without SMDW service
The income level was determined based on the GNI per capita (https://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD). The population without SMDW services per km2 was derived from Lord et al.‘s study25. We calculated the population without SMDW per km2 at a 1-degree resolution and filtered out the 1° grids with values higher than 100 persons km−2, which were converted into points afterward.

Establishing physics-guided machine learning to access the global SMDW yield
Physics-guided machine learning integrates both finite element simulation and the RF model38,39.

The finite element simulation model was constructed by COMSOL Multiphysics. Modules including single-phase flow (spf), heat transfer (ht), moist transfer (mt) and radiation (rad) are applied to simulate the multi-physical processes in the device (Fig. 3a and Supplementary Fig. 20). For simplification, the 3D model was set with zOx as the symmetrical surface.

In the spf module, turbulent flow (spf1) and laminar flow (spf2) were used to describe the airflow in the device and the outside environment, respectively. The wall is no slip. Gravity was included to induce buoyancy. In spf1, an open boundary was used to ensure the mass and momentum conservation of spf1. The headspace was set as moist air. In spf2, the wind speed of inlet 1 is controlled to 3 m s−1 and the right end of this model was set as an open boundary wet net normal stress of 0 N m−2.

In the ht module, heat transfer in solids and fluids (ht1) and heat transfer in fluids (ht2) were used to describe the processes in the device and the outside environment, respectively. The equation is as Eq. (3):

ρCp (∂T/∂t + u·∇T) + ∇·q + qr = αp T (∂p/∂t + u·∇p) + τ:∇u + Q (3)

Where ρ is the density, Cp is the specific heat capacity at constant pressure, T is the absolute temperature, u is the velocity vector, q is the heat flux by conduction, qr is the heat flux by conduction, αp is the coefficient of thermal expansion, p is the pressure, τ is the viscous stress tensor, and Q contains heat sources other than viscous dissipation. In ht1, the headspace was set as moist air, and the water domain was set as a solid to simplify the calculation. Meanwhile, a layer with a thickness of 5 mm was set at the upper surface of the water domain (thermal conductivity 0.05 W m−1 K−1) to describe the layer of solar evaporators and the heat localization effect. Its surface temperature originated from the diffuse surface in the rad module. The boundary of ht1 was set as a single layer (thermally thick) to correspond to the glass top cover (1 mm) and acrylic container (4 mm). The bottom of the ht1 is set as a heat-insulating layer to avoid directing heat from the ground. In ht2, the whole domain was set as moist air, and the temperature was derived from the actual hourly temperature (interpolation functions). The inlets were coupled to the inlets in spf2, and the temperature was also derived from the actual hourly temperature. The bottom of ht2 was set as a heat source coupled to solar irradiance to simulate the effect of the ground.

In the rad module, an out-radiation heat source was applied to simulate the sun. The solar irradiance was obtained by interpolation functions based on the actual hourly shortwave downward irradiance. The light source was set at infinity and the incident direction was controlled to vector (8, 0, −13) for simplicity. The surface of the solar evaporators was set as a diffuse surface.

In the mt module, mt1 and mt2 were used to describe processes in the device and the outside environment, respectively. The equation is as Eq. (4):

∂cv/∂t + u·∇cv + ∇·(-Mv D∇cv) = G (4)

Where M is the molar mass of water vapor, cv is the vapor concentration, D is the vapor diffusion coefficient in air, u is the air velocity field, G is the moisture source (or sink). D was set as a temperature function (D = 0.211 (T/273.15)1.94 × (p0/p)47. In mt1, a wet surface was applied at the top surface of the solar evaporator to initiate evaporation with an evaporation rate factor of 0.08 m s−1. Moist surfaces were applied to the glass top cover to initiate the condensation. The initial liquid water concentration was 0 mol m−2 and the evaporation rate factor was optimized according to the 100-day outdoor water yields. The open boundary was coupled to spf1. In mt2, the initial relative humidity of mt2 was 20%.

In the multi-physics module, mt1 was coupled with ht1 and spf1, while mt2 was coupled with ht2 and spf2.

The finite element model was optimized by the obtained SMDW yield of the solar evaporation pilot study. The hourly surface DSW and air temperature were used to define the environmental conditions of the solar evaporation device. The hourly 1°x1° resolution all-sky surface DSW was obtained from the CER_SYN1deg-1Hour_Terra-Aqua-MODIS_Edition4A dataset, which was retrieved from the Clouds and the Earth’s Radiant Energy System (CERES) instruments onboard Terra and Aqua satellites48. The hourly 0.5°x0.625° resolution surface air temperature data comes from the Modern-Era Retrospective analysis for Research and Applications, Version 2, tavg1_2d_flx_Nx dataset, a 2-dimensional assimilated surface flux diagnostics data collection in Modern-Era Retrospective analysis for Research and Applications version 249. The transparency and condensing rate of the glass top cover were optimized to make the SMDW yield of the physical model output match the results of the pilot study. Fifty-five days were selected as the training dataset, and 10 random days were chosen as the validation data set to evaluate the performance of the SMDW yield physical model.

As shown in Supplementary Fig. 8, based on the finite element model, an RF model was established as follows. We first randomly selected 30 sites (cities) all around the world, whose latitude and longitude are included in Supplementary Table 2. Secondly, for each city, we randomly selected one 10-day period in the first half of each year between 2019 and 2021, and another 10-day period in the latter half of each year. Thirdly, we utilized the physical model (finite element model), with hourly meteorological data used as inputs, to simulate the hourly SMDW yield of both the Eva. opt. and Eva.-cond. opt. models during each of the six 10-day periods. Fourthly, after averaging the hourly meteorological data and SMDW yield to the daily scale, we compiled the 60 daily surface downward solar shortwave radiation (DSW), surface air temperature, and SMDW at all 30 sites. Fifthly, we developed RF models by using the meteorological data on the day of and the day before SMDW production as the predictors and applying the SMDW yield as the training target. So they can relate the DSWs and temperatures on the day and the day before to the daily SMDW yields.

The obtained RF models correspond to both the Eva. opt. and Eva.-cond. opt. models were checked by 10-fold cross-validation. Meteorological data from the whole world (excluding Antarctica) were used as the RF model inputs to obtain the corresponding SMDW yield map. The daily maps were averaged for 3 years (2019–2021) to obtain the annual average SMDW yield map. Six cities, including Mexico City, Abuja, Cairo, Jakarta, New Delhi, and Ulaanbaatar, were selected to show the daily SMDW yield and the seasonal variation across the three years. Then, the maps of the global SMDW yield and the population without SMDW services were combined to show how the SMDW yield matched the population who needed it. Finally, the SMDW yield across the world was also classified according to the country to obtain the annual average SMDW of different countries.

Cost evaluation of SWE
According to the World Bank’s method of calculating the cost of meeting the 2030 SDG-6.1 targets on drinking water, sanitation, and hygiene, the total cost of SWE was separated into three parts, including the capital, maintenance, and operation costs. The discount rate was set at 5%, and all costs were converted to those existing in 2015 for better comparisons with the ideal and current investment data provided by the World Bank26. The capital cost per area is defined as Eq. (5):

The capital cost per area = raw materials ($44.333 m−2) + energy ($0.265 m−2) + facilities ($1.161 m−2) + labor costs (year 2022) (5)

The raw materials refer to the solar evaporators, sodium alginate, polyvinyl alcohol (PVA), poly(methyl methacrylate) and glass used.

The energy cost refers to the manufacturing energy of devices and fabrication of the solar evaporators. The facility cost refers to the necessary parts to make the solar evaporation device work properly, which mainly includes water containers and silicone tubes. The labor cost refers to the manufacturing fees of the solar evaporation device. The details of the capital cost estimation are included in the supplementary information Note 3 (Supplementary Tables 3 and 4).

The labor cost was estimated by setting China as the typical upper-middle-income country with a labor cost of $20.862 m−2, and the labor costs of other counties were converted by the monthly average earnings (https://ilostat.ilo.org/data/) of different income-level countries as divided by the World Bank in 2022. With the raster data of the annual SMDW yield, the corresponding required SMDW supply area per capita was calculated as Eq. (6):

supply area = 2.5 / SMDW yield (6)

and 2.5 L per capita per day is the daily drinking water of a person as instructed by the WHO. Thus, as Eq. (7):

capital expense per capita = SMDW supply area × capital cost per area (7)

The capital cost per capita of each country can be obtained as Eq. (8):

capital cost per capita = capital expense per capita × 1.02 (8)

1.02 contains 2% of the matched software costs.

The operation cost refers to the price of the consumable parts in the solar evaporation device that need to be replaced every 2 years. The maintenance cost refers to the repair of the whole solar evaporation device, whose lifespan is estimated as 10 years50,51. The maintenance costs were estimated as 3% of the capital costs per year and comprised 30% of the capital costs (estimation details are included in the supplementary Note 3).

Therefore, the total cost of each country was obtained as Eq. (9):

Total cost = (capital + maintenance + operation) × population to be served with SMDW (9)

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript reports on the condensation-enhanced strategy and develops a physics-guided machine-learning model for assessing the global potential of SWE technology to meet SMDW demand for unserved populations without external electricity input. The obtained results indicated that a condensation-enhanced SWE device (1 m2) could supply enough drinking water (2.5 L day-1) to 95.8% of the population lacking SMDW. However, the novelty of this work should be carefully considered in comparison to previous works Nature (2021) 598, 611–617\nOther points should be checked:\nLine 105, currently the efficiency is over 100% which is shown in many publications. Please check it\nout\nBy using the solar to water evaporation, the concentration of ions decreased. Please carefully\ndescribe the reason why?\nOther factors such as temperature, humidity, light concentrations, etc. on the efficiency of the\ndevice should be discussed.\nThe calculation of solar to steam efficiency should be discussed as Journal of Power Sources, 2020,\n448, 227388", "reviewer_2": "This study is interesting and fulfills a need in the literature. Atmospheric water harvesting (AWH)\nhas received a good deal of attention, with several studies and global potential estimates in place.\nHowever, solar water evaporation (SWE) is a bit less reviewed. This paper provides a global\npotential and overall starting point for further academic study.\nThe authors take a similar approach to assessing the global potential as was done in other high-\nprofile studies on water harvesting, but apply it to water evaporation. This is good since it allows\ncomparisons of potentials across methods to better understand the challenges of water-related\nSustainable development goals.\nThe authors have identified condensation as the overlooked \"bottleneck\" to improve\nSWE technology. The case is well-made.\nThe analysis of costs and return on investment to reach more people without SMDW using SWE is\ncompelling.\nHere are a few comments/questions/suggestions:\n- I'm unclear on the role of the finite element model in relation to the RF model. What time period\nwas the DSW data sampled? Is 55 days at hourly intervals (1320 data points) sufficient for training a\nmachine learning model? Can the authors comment on this?\n- Where was the data from the 30 worldwide cities derived? From ground measurements? This is\nnot described.\n- The DSW and temperature spatial data from CERES and MERRA 2 should be further described\n(what spatial resolution, sensor?)\n- The units of Fig S9 of DSW should include the spatial normalization (I assume it's kWh/year PER\nMETER?)\n- A little more background on SWE is needed: the authors should identify water purification and\ntreatment as the purpose of SWE, and perhaps differentiate it from AWH.\n- Line 48: \"...encompassing from zero to three dimensions...\" needs further explanation.\n- Line 142: \"...short plank...\" is not a phrase I understand\n- Line 358: \"The solar evaporators were fabricated via the pyrolysis of the sugarcane as reported\nbefore and they could\" doesn't make sense\n- Line 381: The citation of \"Jackson et al\" is a mistake. It should be \"Lord et al\".", "reviewer_3": "This paper investigated the condensation enhanced solar water evaporation (SWE) device and\nproposed a physics-guided machine learning model to unveil the mass‒energy transfer\nmechanisms in SWE devices and assess the safely managed drinking water (SMDW) yield potential\nof the SWE technique using global data. The model prediction part is interesting and shows\noptimistic prospect. However, the discussion on condensation enhanced SWE device seems to be\nmisleading. Besides, there are some important details missing in this paper.\nLine 54: \"The complexity these strategies weakens the SWE inherent merits of low cost, simple\nfacilities and hampers the deployment to the point-of-use water supply\" is one of the important\nclaim to support the development of SWE with enhanced condensation in this work. However, such\nclaim is not rigorous. The water production cost of SWE is decided by the efficiency, cost and\nlifetime simultaneously. Increasing the efficiency and prolonging the lifetime could also reduce the\ncost, as long as the cost is well controlled. Meanwhile, the \"simple facilities\" should not referring to\nthe simple design of SWE device, but the passive operation of SWE device. As long as the device\noperates without additional electricity input and maintenance, it is a simple system.\nLine 105: the author claimed that \"condensation has been overlooked\". However, this is not true,\nsince many studies discussing the condensation of SWE device like: Desalination, 2008, 230(1-3):\n51-61; Solar Energy, 2021, 225: 666-693; Renewable and Sustainable Energy Reviews, 2014, 38:\n309-322; Applied Thermal Engineering, 2022, 215: 118941. Although tremendous progress in\nevaporation enhancement has been witnessed in past years, it does not represent condensation is\noverlooked, since it has been investigated for a long time.\nThe water production rates have obvious degradation in the 100-day operation. Will the device have\nfurther performance degradation in longer-term operation? Could this support the important \"10\nyears\" lifetime of the device?\nAlthough the authors are aware of the importance of \"electricity-free\" system, two cases shown in\nFig.2b still include the use of pump. Even if this could be powered by PV, the moving parts will bring\nadditional maintenance and control systems, which weakens the SWE inherent merits of simple\nfacilities. Please note that the use of vapor pump is quite energy-consuming than liquid pump, and\nthe blade of vapor pump is easy to be broken by droplet.\nThe test results from the outdoor of the five devices are the basis for physics-guided machine\nlearning. The equipment of the test device should be described appropriately. The calculation\nmethod of water yield should be described. The picture of the outdoor test and conditions of the\ncorresponding tests should be provided even in SI.\nHow did the author evaluate the energy efficiency? Did the author consider the solar input for\npower generation for case 3 and case 5?\nThe relationship between physical modeling and machine learning should be presented in a visual\nform to improve the coherence and readability of the paper.\nFig. 2c should be revised. There are errors in the order of columns representing different cases,\nwhich need to be displayed uniformly.\nFig. 4b and Fig. 4c should be revised, which represent the key conclusions of this paper, but the\ncritical information zone is too small to see clearly. It is better to enlarge the key areas to improve\nclarity.\nFor the cost analysis of SWE, the detailed calculation process of the raw materials cost should be\nprovided. The data sources of the \"energy\", \"facilities\" and \"labor costs\" used for the capital cost\ncalculation should also be elaborated."}, "author_response": "Reviewer #1 (Remarks to the Author):\nThe manuscript reports on the condensation-enhanced strategy and develops a physics-\nguided machine-learning model for assessing the global potential of SWE technology\nto meet SMDW demand for unserved populations without external electricity input.\nThe obtained results indicated that a condensation-enhanced SWE device (1 m2) could\nsupply enough drinking water (2.5 L day-1) to 95.8% of the population lacking SMDW.\n1. However, the novelty of this work should be carefully considered in comparison to\nprevious works Nature (2021) 598, 611–617\nResponse: Thank you for your suggestions. The work Nature (2021) 598, 611–617 has\nalready been included in our manuscript. Generally, there are three differences in our\nwork:\n(1) Different models:\nNature (2021) 598, 611–617 proposed a geospatial tool (AWH-Geo) model that\nwas established based on previously reported results in the lab and theoretical\nestimation. Our physics-guided machine learning model integrates the physical model\nand random forest (RF) method and is based on our outdoor experiments under natural\nconditions. The detailed differences are as follows:\nTable The model differences\nDifferent aspects Nature (2021) 598, 611–617 Our work\nPhysical model & random\nModel types AWH-Geo model\nforest (RF) method\nLogistic regression curve fit to Outdoor 100-day solar\nWater yield data\nthe reported SYs of three data evaporation water yield\nsources\npoints in the lab (0.21, 3.71 and tests under natural\n9.28 l kWh-1 at 30, 60 and 90% conditions for 5 different\nRH)1. cases\nTheoretical calculation of the\nspecific water yield according to\nthe Carnot cycle from reference2.\nDaily solar irradiance and\nRelative humidity, temperature\ntemperature on the day of\nInput and solar irradiance during the\nand the day before\nwater harvesting period\nSMDW production\nSpecific purified water\nand the heat and mass\nOutput Specific water yield\ntransfer mechanism inside\nthe SWE devices\n(2) Different analysis of the technique feasibility:\nAs pointed out by reviewer 3, the water production cost of SWE is decided by the\nefficiency, cost and lifetime simultaneously. We simultaneously evaluate the feasibility\nof SWE from three aspects: the design of SWE devices, the annual average SMDW\nyield and the cost of serving 2.5 L SMDW per capita across different designs (Fig. 1\nand 5).\nIn Fig.1, we mapped the populations and the Gross National Income (GNI)\ntogether to point out that poor SMDW coverage is a technical and economic dual-\ncontrolled problem. In Fig. 5 we evaluate the feasibility of SWE both technically and\neconomically by providing the SMDW supply potential and the cost of applying SWE\nto address SDG–6.1.\nIn comparison, cost evaluation is not the key point in Nature (2021) 598, 611–617,\nwhich mainly assesses the potential of addressing SDGs based on the specific water\nyield.\nFig. 1 Geography of the global water-economy nexus.\nFig. 5 Promoting global SMDW coverage by SWE.\n(3) Different application fields:\nNature (2021) 598, 611–617 is in the field of atmospheric water harvesting and\nthe typical two scenarios are sorption-based materials and cooler–condenser. By\ncontrast, our work fits the area of SWE for water purification. Our target is to figure\nout how the evaporation and condensation in SWE device design would affect their\nfeasibility in serving SDG–6.1.\nTherefore, we have revised the introduction section to highlight the different\nphysics-guided machine-learning model we used and the technical and economic\nassessment of the SWE in addressing SDGs–6.1:\n(Manuscript, Line 66–75) A geospatial tool (AWH-Geo) has been proposed to\ncombine the material water yield kinetics of previous reports with dominant\nenvironmental variables to assess the global potential for harvesting drinking water\nfrom the air given available climatic resources25. It pinpoints the maximum impacts of\natmospheric water harvesting to address water scarcity on a global scale, proving a great\nparadigm for evaluating a technique’s contribution to the SDGs. Therefore, it is\nimportant to reconsider the SWE technique for better supplying SMDW to serve SDG–\n6.1. Differently, considering that low-cost and flexible implementation make SWE\nunique, cost and efficiency should be considered to evaluate the feasibility of SWE\nunder natural conditions. An effective tool that could anticipate the technical and\neconomic potential of SWE and, in turn, reveal the technical bottlenecks to guide the\nSWE device design, is useful.\n(Manuscript, Line 76–83) Here, we proposed a physics-guided machine learning\nmodel that integrates the physical model and random forest (RF) method. It could\nsimultaneously unveil the mass‒energy transfer mechanisms in SWE devices and\nassess the SMDW yield potential of the SWE technique both technically and\neconomically. The physical model was based on pilot experiments under natural\nenvironmental conditions to clarify the principle of designing the SWE devices and\nestablish the causality between SWE devices and meteorological parameters. With this\ncausality, the physical model was abstracted to the RF method to simplify the\ncalculation. The cost evaluation was also included and was merged with the SMDW\nyields of SWE and the population without SMDW to inspect the feasibility of SWE.\nAlso, at the beginning of the section “Assessing the global SMDW yield potential\nusing a physics-guided machine learning model”, we clarified the aim of these sections\nas:\n(Manuscript, Line 248–250) To evaluate the technical feasibility of SWE, we first\nmapped the upper limit of the average annual SMDW yield (in L m-2 day-1) under the\nhypothesis the DSW could be used for evaporation at 293.15 K, and all generated vapor\ncould be condensed and collected (Fig. 4a).\nAt the beginning of the section “Extending SMDW service and advancing SDG-\n6.1 by SWE”, we clarified the main points of this section:\n(Manuscript, Line 287–288) Going beyond technical feasibility, the specific cost\nof implementing SWE to supply SMDW is also crucial for extending SMDW service.\nOther points should be checked:\n2. Line 105, currently the efficiency is over 100% which is shown in many publications.\nPlease check it out\nResponse: Thank you for your suggestions. Much progress has been made to elevate\nthe solar-to-vapor efficiency to over 100%. Therefore, we have defined the efficiency\nas solar-to-heat efficiency to precisely refer to the sunlight being converted to heat\nprocess (solar-thermal conversion step), and the sentence has been revised as\n(Manuscript, Line 116–120) “The SWE technique employs three steps to produce\npurified water, including solar-thermal conversion (solar to heat), vaporization (heat to\nvapor) and condensation (vapor to water, Fig. 2a). After decades of efforts, the solar-\nto-heat efficiency has been elevated to over 90%, and the converted heat could then\ninitiate highly efficient vaporization3,4. However, to fulfill the solar-to-water process,\ncondensation is also a critical point that determines the overall SMDW yield28-31.”\nAlso, we have added the description of the solar-to-water efficiency over 100% in\nthe introduction section (Manuscript, Line 55–58) as: “Important developments,\nincluding larger condensing areas, condensing materials with higher thermal\nconductivity, forced condensing and multi-stage devices with latent heat recovery or\ndriven by additional photovoltaics, have been proposed to further SMDW output even\nwith solar-to-water efficiency over 100%15, 19-22.”\n3. By using the solar to water evaporation, the concentration of ions decreased. Please\ncarefully describe the reason why?\nResponse: Thank you for your suggestions. Sorry for failing to make it clear. The water\nwith decreased ion concentration is the condensate.\nWe have revised Fig. 2a to avoid misunderstanding of the ion decrease process.\nAs shown in the revised Fig. 2a, during the solar water evaporation (SWE) process,\nwater has undergone an evaporation and condensation cycle. Water molecules in the\nraw water evaporate to water vapor and get separated from the ions. Then water\nmolecules in the vapor condensed into liquid water (condensate) again with almost no\nions in it.\nIn the manuscript, we have measured the conductivity (Fig. S4) and the main anion\nconcentrations (Fig. S5) of the condensate to evaluate the ion removal performance of\nSWE.\nWe have also revised the introduction to better highlight that Solar water\nevaporation is a technique that could purify raw water:\nSolar water evaporation (SWE) converts solar energy to heat to initiate water\nevaporation to purify water from different sources to supply SMDW. Salts, heavy metal\nions organics and pathogenic microorganisms could be removed from the water. The\nSWE technology is flexible, feasible, cost-and-energy efficient and has a near-zero\ncarbon footprint, which is believed to satisfy SMDW demand in remote areas10-12.\n(Manuscript, Line 45–49)\nRevised Fig. 2a The solar-to-vapor processes and the corresponding energy efficiency.\n100\n25%~75%\nRange within 1.5IQR\nMedian Line\n) Mean\n1\n-m\nc\nS\n60\nμ\n(\ny\nvit\ncti 40\nu\nd\nn\no\nC\n20\nFig. S4 The conductivity of the produced water by SWE.\nFig. S5 The ion concentration of the produced water by SWE. (a) Cl-. (b)SO 2- (The\n4\nred dashed line is the WHO-defined criteria).\n4. Other factors such as temperature, humidity, light concentrations, etc. on the\nefficiency of the device should be discussed.\nResponse: Thank you for your suggestions. We have included temperature, humidity,\nlight concentrations and wind speed as explanatory variables to evaluate their\ncorrelation with the efficiency of the device by redundancy analysis (RDA). Fig. 2e has\nalso been updated. The revised parts in the manuscript are as follows:\n(Manuscript, Line 166–179) This is further demonstrated by performing\nredundancy analysis (RDA). We set the temperature, absolute humidity, wind speed\nand downward shortwave irradiance (DSW) as the explanatory variables and the\nSMDW daily yields of cases 1–5 as the response variables (Fig. 2e). The results show\nthat wind speed shows little relation to the solar SMDW yields (red arrows), while\nabsolute humidity only exhibits a slightly positive relation with SMDW yields.\nCompared to absolute humidity, the angles between the temperature and SMDW yields\ndecrease, demonstrating a stronger positive influence of temperature. This is due to that\nSWE devices could interact with natural conditions by heat exchange, which\ndetermines the condensation inside the device and the heat loss from the device to the\nenvironment. DSW poses the most dominant influence on the SMDW yields with its\ncorrelation coefficient with RDA1 of 0.99. Moreover, through optimizing the\ncondensation, the case 3–5 SWE devices show more strongly positive relations to the\nDSW compared to the case 1 and 2 SWE devices, corresponding to their higher SMDW\nyields (Fig. 2c). Amony them, case 4 tops the positive correlation with DSW, which\nagrees well with its best solar energy utilization efficiency of case 4 (Fig. 2d), proving\ncondensation could make the SWE device better utilize solar energy to produce SMDW.\n4\n2\n)\n%\nWind speed\n1 . Case 3\n( 0\nA\nD Absolute Downward shortwave\nR humidity irradiance\n-2\nTemperature\n-4\n-1 0 1 2\nRDA 1 (98.74%)\nRevised Fig. 2e RDA between the meteorological parameters and the SMDW yield.\n5. The calculation of solar to steam efficiency should be discussed as Journal of Power\nSources, 2020, 448, 227388\nResponse: Thank you for your suggestions. We have added the reference “Journal of\nPower Sources, 2020, 448, 227388” to help readers for a better understanding of the\nSMDW yield energy efficiency. The energy efficiency calculation methods are\nincluded in the “Methods” section of the manuscript as:\n(Manuscript, Line 414–421) “The SMDW yield and energy efficiency calculations.\nThe solar-to-water energy efficiency was calculated as the ratio between the heat of the\ngenerated vapor and the consumed energy:\nṁ×h\nfg\nη =\nsolar-water q +q\nsolar electricity\nWhere, ṁ is mass flow of the SMDW yield (kg m-2), h is the latent heat (kJ kg-1),\nfg\nq is the incident solar intensity (kJ m-2) and q is the energy consumption\nsolar electricity\nintensity (kJ m-2). The power of the vapor pump is 5 W and works all day to pump the\nheadspace water vapor in a work area of 17×17 cm2.”\nWe also took the suggestions of reviewer 3# (Question 6) to include the solar input for\npower generation for case 3 and case 5 by calculating the energy consumption of the\nvapor pump.\nAs a result, the energy efficiency of the Fig. 2d was updated as follows. We also\nupdate the analysis in the manuscript as (Line 154–158) “Comparatively, pumping out\nthe vapor through the condensing tube elevates the SMDW yield of cases 3 and 5, but\ntheir energy efficiency only ranges from 1.9–37.1% and 5.7–38.1% by taking the energy\nconsumption of the vapor pump into consideration, respectively, which is even\ninferior to the case 2 device without enhanced condensation.”\n100\n80\n)\n%\n(\ny 60\nc\nn\ne\nci\n40\nfi\nf\ne\ny\ng 20\nr\ne\nn\nE\n0\n-20\nCase 1 Case 2 Case 3 Case 4 Case 5\nRevised Fig. 2d Statistical distribution of the solar energy utilization efficiency of all\ncases.\nReviewer #2 (Remarks to the Author):\nThis study is interesting and fulfills a need in the literature. Atmospheric water\nharvesting (AWH) has received a good deal of attention, with several studies and global\npotential estimates in place. However, solar water evaporation (SWE) is a bit less\nreviewed. This paper provides a global potential and overall starting point for further\nacademic study.\nThe authors take a similar approach to assessing the global potential as was done in\nother high-profile studies on water harvesting, but apply it to water evaporation. This\nis good since it allows comparisons of potentials across methods to better understand\nthe challenges of water-related Sustainable development goals.\nThe authors have identified condensation as the overlooked \"bottleneck\" to improve\nSWE technology. The case is well-made.\nThe analysis of costs and return on investment to reach more people without SMDW\nusing SWE is compelling.\nResponse: Thank you for your kind comments.\nHere are a few comments/questions/suggestions:\n1. I'm unclear on the role of the finite element model in relation to the RF model. What\ntime period was the DSW data sampled? Is 55 days at hourly intervals (1320 data points) \nsufficient for training a machine learning model? Can the authors comment on this?\nResponse: Sorry for the missing information that made you confused. We have revised\nthe description of the physics-guided machine learning in the methods section of the\nmanuscript (Line 485-505) as:\nAs shown in Fig. S8, based on the finite element model, an RF model was\nestablished as follows. We first randomly selected 30 sites (cities) all around the world,\nwhose latitude and longitude are included in Table S2. Secondly, for each city, we\nrandomly selected one 10-day period in the first half of each year between 2019 and\n2021, and another 10-day period in the latter half of each year. Thirdly, we utilized the\nphysical model (finite element model), with hourly meteorological data used as inputs,\nto simulate the hourly SMDW yield of both the Eva. opt. and Eva.-cond. opt. models\nduring each of the six 10-day periods. Fourthly, after averaging the hourly\nmeteorological data and SMDW yield to the daily scale, we compiled the 60 daily\nsurface downward solar shortwave radiation (DSW), surface air temperature and\nSMDW at all 30 sites. Fifthly, we developed RF models by using the meteorological\ndata on the day of and the day before SMDW production as the predictors and applying\nthe SMDW yield as the training target. So they can relate the DSWs and temperatures\non the day and the day before to the daily SMDW yields.\nThe obtained RF models corresponding to both the Eva. opt. and Eva.-cond. opt.\nmodels were checked by a 10-fold cross-validation. Meteorological data from the whole\nworld (excluding Antarctica) were used as the RF model inputs to obtain the\ncorresponding SMDW yield map. The daily maps were averaged for 3 years (2019–\n2021) to obtain the annual average SMDW yield map. Six cities, including Mexico City,\nAbuja, Cairo, Jakarta, New Delhi and Ulaanbaatar, were selected to show the daily\nSMDW yield and the seasonal variation across the three years. Then, the maps of the\nglobal SMDW yield and the population without SMDW services were combined to\nshow how the SMDW yield matched the population who needed it. Finally, the SMDW\nyield across the world was also classified according to the country to obtain the annual\naverage SMDW of different countries.\nHence, the number of data samples available for RF model training is (10-1)×6×30\n= 1620. Because these data samples contain the meteorological conditions and water\nyield information across different seasons and different regions, and the number of\ntraining samples is generally sufficient for a simple machine learning model (e.g., RF\nmodel)5,6, we expect that this RF model can be suitable for daily SMDW yield\nsimulation all year round over the world.\nTo justify the reliability of this RF model, we performed ten-fold cross-validation.\n‘Ten-fold’ means we divided all the training data into ten parts, used 9 of those parts\nfor training while reserving one-tenth for testing each time, and repeated this procedure\n10 times to derive ten separate RF models. Ten-fold cross-validation has proven to be\na reliable method for machine learning model accuracy assessment7. We added in the\nmain text (Line 231-234):\nThe RF models showed predicting R2 values of 0.97±0.0068 and 0.99±0.0071 for\nthe Eva. opt. and Eva.-cond. opt. models, respectively, and the root-mean-square errors\n(RMSEs) are 0.22 and 0.27 L m-2 d-1, demonstrating that the RF models have the\npotential to predict the global SMDW yield.\nFig. S8 Data processing workflow of the physics-guided machine learning model.\nCylinders indicate the data from the pilot study, CERES and MERRA 2. The rest are\ntable frames (the output documents), rhombus (judgments of the model), rectangles\n(data processing), and parallelograms (output datasets).\nTable S2. Locations of the selected 30 cities over the world\nCity Longitude Latitude\nTijuana -115.7439 31.7774\nLima -76.4472 -12.3607\nSao Paulo -46.5646 -23.5229\nRecife -35.0949 -8.0802\nGuatemala -90.4729 14.5922\nCancun -86.8254 21.0774\nLagos 3.4156 6.5736\nDakar -17.3282 14.7607\nCairo 31.3193 30.0944\nAddis Ababa 38.7241 9.0008\nZabid 43.3325 14.1623\nKampala 32.5934 0.3286\nBlantyre 34.9884 -15.846\nCape Town 18.6595 -33.904\nAdelaide 138.7666 -34.9316\nKabul 69.377 34.5693\nNew Delhi 77.1993 28.6996\nColombo 79.9441 7.1402\nBandung 107.7573 -6.5379\nManila 120.809 14.6857\nUrumqi 87.5864 43.8195\nUlaanbaatar 106.8674 48.0789\nSaint Petersburg 30.4245 59.9336\nArkhangelsk 40.488 64.5961\nNovokuznetsk 87.1117 53.7366\nPyongyang 125.8023 39.0779\nLisbon -9.1743 38.766\nPhoenix -112.0436 33.4795\nVancouver -123.1398 49.3626\nParis 2.5218 48.9748\n2. Where was the data from the 30 worldwide cities derived? From ground\nmeasurements? This is not described.\nResponse: Thank you for your suggestions. As aforementioned, the description of the\nphysics-guided machine learning in the methods section of the manuscript was revised\nto include the sources of the data (Line 485-505):\nAs shown in Fig. S8, based on the finite element model, a RF model was\nestablished as follows. We first randomly selected 30 sites (cities) all around the world,\nwhose latitude and longitude are included in Table S2. Secondly, for each city, we\nrandomly selected one 10-day period in the first half of each year between 2019 and\n2021, and another 10-day period in the latter half of each year. Thirdly, we utilized the\nphysical model (finite element model), with hourly meteorological data used as inputs,\nto simulate the hourly SMDW yield of both the Eva. opt. and Eva.-cond. opt. models\nduring each of the six 10-day periods. Fourthly, after averaging the hourly\nmeteorological data and SMDW yield to the daily scale, we compiled the 60 daily\nsurface downward solar shortwave radiation (DSW), surface air temperature and\nSMDW at all 30 sites. Fifthly, we developed RF models by using the meteorological\ndata on the day of and the day before SMDW production as the predictors and applying\nthe SMDW yield as the training target. So they can relate the DSWs and temperatures\non the day and the day before to the daily SMDW yields.\nThe obtained RF models corresponding to both the Eva. opt. and Eva.-cond. opt.\nmodels were checked by a 10-fold cross-validation. Meteorological data from the whole\nworld (excluding Antarctica) were used as the RF model inputs to obtain the\ncorresponding SMDW yield map. The daily maps were averaged for 3 years (2019–\n2021) to obtain the annual average SMDW yield map. Six cities, including Mexico City,\nAbuja, Cairo, Jakarta, New Delhi and Ulaanbaatar, were selected to show the daily\nSMDW yield and the seasonal variation across the three years. Then, the maps of the\nglobal SMDW yield and the population without SMDW services were combined to\nshow how the SMDW yield matched the population who needed it. Finally, the SMDW\nyield across the world was also classified according to the country to obtain the annual\naverage SMDW of different countries.\nTherefore, the data from the 30 worldwide cities was the simulated data of the\nfinite element model, and this physical finite element model was trained and verified\nby the measured 100-day SMDW yield. The physical finite element model simulates\nthe SMDW yield based on the precise equation to calculate the mass and energy transfer\nand transformation inside the equipment, which could be demonstrated by the both the\nSMDW yield and the inner conditions (temperature, humidity, evaporation rate) of the\nSWE devices (Fig. 3). In contrast, the random forest (RF) model predicts the SMDW\nyield based on the mathematical relationship between meteorological conditions and\nthe simulated SMDW yield using the physical model. Meanwhile, the RF model saves\ncomputing power and is more suitable for assessing the global SMDW supply potential.\nFig. 3 Finite element simulation of the SWE system. (a) The inner air temperature,\nvapor concentration, solar evaporator surface temperature (inset) and evaporation rate\n(inset) of the SWE devices. (b) Comparisons of the headspace vapor concentration and\nthe solar evaporator surface evaporation rate between the Eva. opt. and Eva.-cond. opt.\nmodels. (c) Fitting of the Eva. opt. model’s simulated SMDW yield against the observed\nvalues. (d) Comparisons between the Eva. opt. model’s daily SMDW yield predictions\nand the observed values. (e) Linear correlation between the accumulated SMDW yield\nsimulation of the Eva. opt. model and the observed value. (f) Fitting of the Eva.-cond.\nopt. model simulated the SMDW yield rate against the observed value. (g) Comparisons\nbetween the Eva.-cond. opt. model’s daily SMDW yield predictions and the observed\nvalue. (h) Linear correlation between the accumulated SMDW yield simulation by the\nEva.-cond. opt. model and the observed value.\n3. The DSW and temperature spatial data from CERES and MERRA 2 should be further\ndescribed (what spatial resolution, sensor?)\nResponse: Thank you for your suggestions. We have added this information in the\nmethods section (Line 474-480) as “The hourly surface DSW and air temperature were\nused to define the environmental conditions of the solar evaporation device. The hourly\n1° x 1° resolution all-sky surface DSW was obtained from the CER_SYN1deg-\n1Hour_Terra-Aqua-MODIS_Edition4A dataset, which was retrieved from the Clouds\nand the Earth's Radiant Energy System (CERES) instruments onboard Terra and Aqua\nsatellites45. The hourly 0.5° x 0.625° resolution surface air temperature data comes from\nthe MERRA-2 tavg1_2d_flx_Nx dataset, a 2-dimensional assimilated surface flux\ndiagnostics data collection in Modern-Era Retrospective analysis for Research and\nApplications version 246.”\n4. The units of Fig S9 of DSW should include the spatial normalization (I assume it's\nkWh/year PER METER?)\nResponse: Thank you for pointing out our mistake. We have revised the Fig. S11 as\nfollows:\nFig. S11 Global distribution of the surface downward shortwave irradiation (DSW).\n5. A little more background on SWE is needed: the authors should identify water\npurification and treatment as the purpose of SWE, and perhaps differentiate it from\nAWH.\nResponse: Thank you for your suggestions. UNEP and we both think that poor water\nmanagement is the key that leads to limited SMDW availability. Poor water\nmanagement refers to scientific water source exploitation, effective water treatment\ntechniques and reliable water distribution. Therefore, taking your suggestions, after\nclarifying the fact that people tend to live near the water sources, we have further\nclarified water management to water treatment techniques to highlight the treatment\npurpose of SWE. The revisions are as follows:\n(Manuscript Line 33-39) However, over 2 billion people still suffer from unsafe\ndrinking water by 2015, which mainly arises from limited water treatment and poor\nwater management. This situation has become particularly acute for populations in\nremote areas, who are concurrently threatened by unmanaged water sources, poverty,\nunderdeveloped purification technology, and isolated population distribution.\nTraditional routes of centralized water treatment to ensure SMDW are energy- and\ncapital-intensive and rely on the scale advantage to minimize the treatment cost.\n(Manuscript Line 45-49) Solar water evaporation (SWE) converts solar energy to\nheat to initiate water evaporation to purify water from different sources to supply\nSMDW. Salts, heavy metal ions organics and pathogenic microorganisms could be\nremoved from the water. The SWE technology is flexible, feasible, cost-and-energy\nefficient and has a near-zero carbon footprint, which is believed to satisfy SMDW\ndemand in remote areas10-12.\n6. Line 48: \"...encompassing from zero to three dimensions...\" needs further explanation.\nResponse: Thank you for your suggestions. We explained in the introduction section\n(Line 49-53) as: “Solar evaporators, encompassing 0D/1D suspended evaporators (e.g.\nmetal nanoparticles) to 2D interfacial evaporation film (e.g. carbon cloth) and then to\n3D evaporators with larger surface areas (e.g. umbrella and tree-shaped designs) have\nbeen proposed with a solar-to-vapor efficiency of over 90% under natural irradiance\n(~1 kW m-2 h-1) across different water body types (sewage, seawater, brackish water,\netc.)13,14.”\n7. Line 142: \"...short plank...\" is not a phrase I understand\nResponse: Thank you for your suggestions. We have replaced the word “short plank”\nwith “bottleneck”, and the sentence now has been revised as “Condensation is the\nbottleneck and how to improve it dominates the SMDW yield more profoundly.”\n(Manuscript Line 162-163)\n8. Line 358: \"The solar evaporators were fabricated via the pyrolysis of the sugarcane\nas reported before and they could\" doesn't make sense\nResponse: Thank you for pointing out our mistakes. We have complemented this\nsentence as “The solar evaporators were fabricated via the pyrolysis of the sugarcane\nas reported before and they could utilize ~97% of solar energy42.” (Manuscript Line\n391-393)\n9. Line 381: The citation of \"Jackson et al\" is a mistake. It should be \"Lord et al\".\nResponse: Thank you for pointing out our mistakes. We have revised the citation to\n“The population without SMDW services per km2 was derived from Lord et al.'s\nstudy25.” (Manuscript Line 423-424)\nReviewer #3 (Remarks to the Author):\nThis paper investigated the condensation enhanced solar water evaporation (SWE)\ndevice and proposed a physics-guided machine learning model to unveil the mass‒\nenergy transfer mechanisms in SWE devices and assess the safely managed drinking\nwater (SMDW) yield potential of the SWE technique using global data. The model\nprediction part is interesting and shows optimistic prospect. However, the discussion\non condensation enhanced SWE device seems to be misleading. Besides, there are some\nimportant details missing in this paper.\n1. Line 54: \"The complexity these strategies weakens the SWE inherent merits of low\ncost, simple facilities and hampers the deployment to the point-of-use water supply\" is\none of the important claim to support the development of SWE with enhanced\ncondensation in this work. However, such claim is not rigorous. The water production\ncost of SWE is decided by the efficiency, cost and lifetime simultaneously. Increasing\nthe efficiency and prolonging the lifetime could also reduce the cost, as long as the cost\nis well controlled. Meanwhile, the \"simple facilities\" should not referring to the simple\ndesign of SWE device, but the passive operation of SWE device. As long as the device\noperates without additional electricity input and maintenance, it is a simple system.\nResponse: Thank you for your suggestions. We strongly agree with your opinion that\nthe water production cost of SWE is decided by the efficiency, cost and lifetime\nsimultaneously, and as long as the device operates without additional electricity input\nand maintenance, it is a simple system.\nWe have revised the introduction (Line 55-63) as “Important developments,\nincluding larger condensing areas, condensing materials with higher thermal\nconductivity, forced condensing and multi-stage devices with latent heat recovery or\ndriven by additional photovoltaics, have been proposed to further SMDW output even\nwith solar-to-water efficiency over 100%15, 19-22. However, the water production cost of\nSWE is decided by the efficiency, cost and lifetime simultaneously. Advanced solar\nevaporators and condensing surfaces tend to increase the cost of raw materials. Forced\ncondensation requires additional electricity input, which weakens the SWE inherent\nmerits of low-cost, flexible implementation and hampers its feasibility to the point-of-\nuse water supply, especially in remote areas23.”\n2. Line 105: the author claimed that \"condensation has been overlooked\". However,\nthis is not true, since many studies discussing the condensation of SWE device like:\nDesalination, 2008, 230(1-3): 51-61; Solar Energy, 2021, 225: 666-693; Renewable\nand Sustainable Energy Reviews, 2014, 38: 309-322; Applied Thermal Engineering,\n2022, 215: 118941. Although tremendous progress in evaporation enhancement has\nbeen witnessed in past years, it does not represent condensation is overlooked, since it\nhas been investigated for a long time.\nResponse: Thank you for your suggestions. We have replaced this sentence to avoid\nmisleading. The four references you recommended have also been included in the\nmanuscript to better support our analysis (Line 117-122).\n“After decades of efforts, the solar-to-heat efficiency has been elevated to over 90%,\nand the converted heat could then initiate highly efficient vaporization13, 14. However,\nto fulfill the solar-to-water process, condensation is also a critical point that determines\nthe overall SMDW yield28-31. Simultaneously evaluating different strategies of\ncondensation and evaporation could help cost-effectively promote global potential\nestimation of SWE for SMDW services10, 16, 32.”\n3. The water production rates have obvious degradation in the 100-day operation. Will\nthe device have further performance degradation in longer-term operation? Could this\nsupport the important \"10 years\" lifetime of the device?\nResponse: Thank you for your suggestions. As shown in Fig. 2c, the SMDW yield seem\nto decrease versus days, which is due to the solar irradiance are getting weaker during\nseason change. We added an analysis in the manuscript to specify the stable solar\nenergy conversion efficiency of the case 4 device as (Manuscript Line 146-150):\n“The energy efficiency of case 4 shows no relation (Fig. S7a, Table S1, p=0.44,\nnot significant) to the day within the 100-day successive SMDW production test.\nInstead, the energy efficiency shows significant positive relations (Fig. S7b, Table S1,\np=4.7×10-4) to the solar irradiance, demonstrating that the case 4 device operates stably\nwith almost no deterioration.”\nFig. S7, Table S1 and the Spearman coefficients calculation methods have been\ninserted into the supplementary materials.\nMoreover, the proposed device is mainly composed of poly(methyl methacrylate).\nIt is very resistant to UV radiation and other weathering, which possesses a long life\nwith manufacturers' estimated life for many grades 10-20 years even in tropical climates.\nIn some cases poly(methyl methacrylate) could withstand over 20 years of seaside\nweather involving powerful summer sunshine, the corrosive effects of the salty seaside\nenvironment, and winds up to 100mph8,9. Thus, we estimated the life of our solar\nevaporation device to be 10 years is reasonable, and the maintenance cost has also been\nincluded (30% of the capital costs10) to account for the replacement and fixing of certain\nparts of the devices.\nFig. S7 The correlation between solar-water energy efficiency and (a) Day, (b) Solar\nirradiance.\nTable S1 The Spearman coefficients between solar-water energy efficiency and Day\nor Solar irradiance\nSpearman coefficients Day Solar irradiance\nSolar-water energy efficiency -0.078 0.34\np-value 0.44 4.7×10-4, ***\n(Supplementary Information, Line 54-60) Correlation analysis was implemented to test\nfor the effects of day and solar irradiance on the solar-water energy efficiency. The data\nregarding solar irradiance and solar-water energy efficiency were ln transformed before\nanalysis to minimize the impacts of the outliers. The results show that the solar-water\nenergy efficiency has no significant relation with the pilot experiment day with a p-\nvalue of 0.44. On the contrary, the Spearman coefficient is 0.34 between the efficiency\nand the solar irradiance with a p-value of 4.7×10-4. Therefore, the solar-water energy\nefficiency of case 4 is stable and no obvious deterioration happens.\n4. Although the authors are aware of the importance of \"electricity-free\" system, two\ncases shown in Fig.2b still include the use of pump. Even if this could be powered by\nPV, the moving parts will bring additional maintenance and control systems, which\nweakens the SWE inherent merits of simple facilities. Please note that the use of vapor\npump is quite energy-consuming than liquid pump, and the blade of vapor pump is easy\nto be broken by droplet.\nResponse: Thank you for your suggestions. We strongly agree with your opinion that\nthe pump would bring additional maintenance and control systems. In our research, we\nset five cases to compare different strategies to obtain SMDW. We have made it clear\nin the manuscript (Line 198-199) that “We take the case 2 SWE device as an example\nof the only evaporation-optimized case (Eva. opt.) and the case 4 SWE device as the\nevaporation-condensation-optimized case (Eva.-cond. opt.).” We also reinforced this\nprecondition in the “Assessing the global SMDW yield potential using a physics-guided\nmachine learning model” section (Line 262-263) as “Then, the annual SMDW yields\nof two typical electricity-free scenarios (the Eva. opt. and Eva.-cond. opt. models) were\nmapped.”\nBy comparison, case 4 tops the SMDW generating efficiency, which only employs\nsolar evaporator and a coated glass top cover (condensation-enhanced) to condense the\nvapor without external energy input.\nThen, we build two physics-guided machine learning models to predict the global\nSMDW supply potential, which corresponds to the case 2 and case 4, respectively. As\ndefined in the manuscript, “Setup of solar evaporation devices” section (Manuscript\nLine 388-402):\nCase 1 was a reference system without solar evaporators. Sunlight is directly\nirradiated into the bulk water to accelerate evaporation and the generated vapor is then\ncondensed on the glass top cover to produce water. Case 2 included solar evaporators\nto float on the bulk water compared to case 1.\nCase 4 includes solar evaporators and used a coated glass top cover (condensation-\nenhanced) to condense the vapor without external energy input. This coating could\nensure the condensed droplets are quickly shed from the glass top cover, so the\ncondensing active sites could be regenerated for enhanced condensation. The\nfabrication method for the coating layer is included in Supplementary document34.\n(5) Case 5 included solar evaporators and integrated both the condensing tube\nimmersed in the bulk water and the coated glass top cover for condensation. The\nheadspace vapor was pumped through the condensing tube immersed in the bulk\nwater for forced condensation with additional photovoltaics and a vapor pump. The\ncondensed water on the coated glass top cover and condensing tube comprised the\nproduced water.\nTherefore, the two models (Eva. opt. and Eva.-cond. opt. models) do not rely on\nadditional facilities powered by electricity. All the analyses in “Assessing the global\nSMDW yield potential using a physics-guided machine learning model” and\n“Extending SMDW service and advancing SDG-6.1 by SWE” sections are free of\nelectricity, corresponding to the simple devices.\n5. The test results from the outdoor of the five devices are the basis for physics-guided\nmachine learning. The equipment of the test device should be described appropriately.\nThe calculation method of water yield should be described. The picture of the outdoor\ntest and conditions of the corresponding tests should be provided even in SI.\nResponse: Thank you for your suggestions. We have added the picture of the outdoor\ntest and conditions as Fig S3. In the main text (Line 122-123), we added “We set five\ncases to differentiate the keys for SWE operation (Fig. S3)” for better understanding.\nWe have also revised the description of the “Setup of the solar evaporation devices”\nas follows to include the outdoor experiment setups and the calculation method of water\nyield (Manuscript Line 374-413).\nThe solar evaporation device was designed with a cuboid-shaped container and a\nwedge-shaped top cover (Fig. S20). The container consisted of an acrylic water tank\nand a glass top cover. The projection area of the devices is 17×17 cm2. The top of the\nbackboard is 29 cm in height and the inclination of the glass top cover is ~ 22.3°. All\ndevice types were installed on the roof of one building in Beijing, China (Fig. S3). The\nwhole device was well-sealed and insulated from the ground to avoid direct ground\nheating. In the devices, the temperature and humidity sensors (TH10S-B, MIAOXIN)\nwere fixed at the top of the backboard, which was connected to an Internet of Things\n(USR-G781-43, Jinan USR IOT Technology Limited) for data recording. A water\nstorage tank (35×35×45 cm) was used to distribute the raw water to the above 5 devices\nby gravity and the water level in each container was controlled by a level control valve\nto 16.0 cm. The raw water was made of artificial brackish water composed of NaCl\n(1.0006 g L-1), CaCl (0.2775 g L-1), Na SO (0.4925 g L-1), and MgCl ·6H O (0.4177\n2 2 4 2 2\ng L-1). Diversion trenches were fixed on the four walls of the container to collect the\ncondensed water. The condensed water then got out of the devices through a single\nsilicone tube or a condensing tube immersed in the water, which varied with the 5\ndevice types in our pilot study (Fig. 2b).\n(1) Case 1 was a reference system without solar evaporators. Sunlight is directly\nirradiated into the bulk water to accelerate evaporation and the generated vapor is\nthen condensed on the glass top cover to produce water.\n(2) Case 2 included solar evaporators to float on the bulk water compared to case 1.\nThe solar evaporators were fabricated via the pyrolysis of the sugarcane as reported\nbefore and they could utilize ~97% of solar energy42.\n(3) Case 3 included solar evaporators and further pumped the headspace vapor through\na condensing tube immersed in the bulk water for forced condensation with\nadditional photovoltaics and vapor pump. The condensed water on the glass top\ncover and condensing tube comprised the produced water.\n(4) Case 4 included solar evaporators and used a coated glass top cover (condensation-\nenhanced) to condense the vapor without external energy input. This coating could\nensure the condensed droplets are quickly shed from the glass top cover, so the\ncondensing active sites could be regenerated for enhanced condensation. The\nfabrication method for the coating layer is included in Supplementary document34.\n(5) Case 5 included solar evaporators and integrated both the condensing tube\nimmersed in the bulk water and the coated glass top cover for condensation. The\nheadspace vapor was pumped through the condensing tube immersed in the bulk\nwater for forced condensation with additional photovoltaics and a vapor pump. The\ncondensed water on the coated glass top cover and condensing tube comprised the\nproduced water.\nThe daily water output was calculated as:\nmass of daily collected water (kg)\nSMDW yield (kg m-2 day-1) =\nevaporation area (m2)\nWhere the mass of the daily collected water is measured by a graduated cylinder at\n18:00 every day. The conductivity of the produced water from each device was tested\nby a conductivity meter (S230, METTLER). The ion concentration was determined by\nanion chromatography (Aquion, Thermo Fisher).\nFig. S3 The picture of the setups of the case 1–5.\n6. How did the author evaluate the energy efficiency? Did the author consider the solar\ninput for power generation for case 3 and case 5?\nResponse: Thank you for your suggestions. We have included the solar input for power\ngeneration for case 3 and case 5 by calculating the energy consumption of the vapor\npump. The energy efficiency calculation methods are included in the “Methods” section\nin the manuscript as (Line 414-421):\n“The SMDW yield and energy efficiency calculations.\nThe solar-to-water energy efficiency was calculated as the ratio between the heat of the\ngenerated vapor and the consumed energy:\nṁ×h\nfg\nη =\nsolar-water q +q\nsolar electricity\nWhere, ṁ is mass flow of the SMDW yield (kg m-2), h is the latent heat (kJ kg-1),\nfg\nq is the incident solar intensity (kJ m-2) and q is the energy consumption\nsolar electricity\nintensity (kJ m-2). The power of the vapor pump is 5 W and works all day to pump the\nheadspace water vapor in a work area of 17×17 cm2.”\nAs a result, the energy efficiency of the Fig. 2d was updated as follows. We also\nupdate the analysis in the manuscript (Line 154-158) as “Comparatively, pumping out\nthe vapor through the condensing tube elevates the SMDW yield of case 3 and 5, but\ntheir energy efficiency only ranges from 1.9–37.1% and 5.7–38.1% by taking the energy\nconsumption of the vapor pump into consideration, respectively, which is even\ninferior to the case 2 device without enhanced condensation.”\n100\n80\n)\n%\n(\ny 60\nc\nn\ne\nci\n40\nfi\nf\ne\ny\ng 20\nr\ne\nn\nE\n0\n-20\nCase 1 Case 2 Case 3 Case 4 Case 5\nRevised Fig. 2d Statistical distribution of the solar energy utilization efficiency of all\ncases.\n7. The relationship between physical modeling and machine learning should be\npresented in a visual form to improve the coherence and readability of the paper.\nResponse: Thank you for your suggestions. We have revised Fig. S8 to make the\nrelation of physical modeling and machine learning more intuitive for the reader to\nunderstand.\nTo justify the reliability of this RF model, we performed ten-fold cross-validation.\n‘Ten-fold’ means we divided all the training data into ten parts, used 9 of those parts\nfor training while reserving one-tenth for testing each time, and repeated this procedure\n10 times to derive ten separate RF models. Ten-fold cross-validation has proven to be\na reliable method for machine learning model accuracy assessment7. We added in the\nmain text (Line 231-234):\nThe RF models showed predicting R2 values of 0.97±0.0068 and 0.99±0.0071 for\nthe Eva. opt. and Eva.-cond. opt. models, respectively, and the root-mean-square errors\n(RMSEs) are 0.22 and 0.27 L m-2 d-1, demonstrating that the RF models have the\npotential to predict the global SMDW yield.\nWe have also revised the description of the setup of the physics-guided machine\nlearning in the methods section of the manuscript (Line 485-505):\nAs shown in Fig. S8, based on the finite element model, an RF model was\nestablished as follows. We first randomly selected 30 sites (cities) all around the world,\nwhose latitude and longitude are included in Table S2. Secondly, for each city, we\nrandomly selected one 10-day period in the first half of each year between 2019 and\n2021, and another 10-day period in the latter half of each year. Thirdly, we utilized the\nphysical model (finite element model), with hourly meteorological data used as inputs,\nto simulate the hourly SMDW yield of both the Eva. opt. and Eva.-cond. opt. models\nduring each of the six 10-day periods. Fourthly, after averaging the hourly\nmeteorological data and SMDW yield to the daily scale, we compiled the 60 daily\nsurface downward solar shortwave radiation (DSW), surface air temperature and\nSMDW at all 30 sites. Fifthly, we developed RF models by using the meteorological\ndata on the day of and the day before SMDW production as the predictors and applying\nthe SMDW yield as the training target. So they can relate the DSWs and temperatures\non the day and the day before to the daily SMDW yields.\nThe obtained RF models corresponding to both the Eva. opt. and Eva.-cond. opt.\nmodels were checked by a 10-fold cross-validation. Meteorological data from the whole\nworld (excluding Antarctica) were used as the RF model inputs to obtain the\ncorresponding SMDW yield map. The daily maps were averaged for 3 years (2019–\n2021) to obtain the annual average SMDW yield map. Six cities, including Mexico City,\nAbuja, Cairo, Jakarta, New Delhi and Ulaanbaatar, were selected to show the daily\nSMDW yield and the seasonal variation across the three years. Then, the maps of the\nglobal SMDW yield and the population without SMDW services were combined to\nshow how the SMDW yield matched the population who needed it. Finally, the SMDW\nyield across the world was also classified according to the country to obtain the annual\naverage SMDW of different countries.\nFig. S8 Data processing workflow of the physics-guided machine learning model.\nCylinders indicate the data from the pilot study, CERES and MERRA 2. The rest are\ntable frames (the output documents), rhombus (judgments of the model), rectangles\n(data processing), and parallelograms (output datasets).\n8. Fig. 2c should be revised. There are errors in the order of columns representing\ndifferent cases, which need to be displayed uniformly.\nResponse: Thank you for your suggestions. For better understanding, we have revised\nthe original Fig. 2c with a new one (Revised Fig. 2c).\nWe have checked the original Fig. 2c, which is an overlapped (not a stacked) bar\nplot to show the water collection rate of cases 1-5. As the variation of the daily SMDW\nyield, the case with the highest SMDW yield would be arranged at the bottom layer and\nthe second highest SMDW yield would be arranged over it and then the third, fourth\nand fifth highest would be arranged successively. Therefore, the order of the columns\ncould be different.\nNow, in the revised Fig. 2c, we have separated cases 1-5 into different y-axis to\nmake the display more uniform.\n8 -1\ny\n) 7 d a\n6\n-2\nm\ng\n5\ne\n( k\n4\nn\nr\nat\n3 eti o\no\n2 oll\nc\n100\n1\nat\ner\n80 0 W\nCase 1 60\nCase 2\nCase 4 20\nCase 5\nRevised Fig. 2c Daily SMDW yield of all cases during a 100-day successive pilot study.\n-1) y\n8\na Case 1\n-2\nd\nm6\nC\nC\na\na\ns\ns\ne\ne\n2\n3\nk g\nC\nC\na\na\ns\ns\ne\ne\n4\n5\n(\ne\nn\nr\nat\n4\no\neti\no\noll2\nc\ner\nat\nW0\n10 20 30 40 50 60 70 80 90 100\nDay\nOriginal Fig. 2c Daily SMDW yield of all cases during a 100-day successive pilot\nstudy.\n9. Fig. 4b and Fig. 4c should be revised, which represent the key conclusions of this\npaper, but the critical information zone is too small to see clearly. It is better to enlarge\nthe key areas to improve clarity.\nResponse: Thank you for your suggestions. We have enlarged the key areas to improve\nclarity.\nFig. 4 The potential SMDW yield of solar evaporation and the distribution of the\npopulation without the SMDW. (a) The upper limit of the mean daily SMDW yield of\nsolar evaporation (the solar-to-vapor efficiency is 100%, without heat recovery). The\ninsets are the seasonal variations in 6 representative cities across the world. The\npredicted mean daily SMDW yield of (b) Eva. opt. and (c) Eva.-cond. opt.\nmodel.\n10. For the cost analysis of SWE, the detailed calculation process of the raw materials\ncost should be provided. The data sources of the \"energy\", \"facilities\" and \"labor costs\"\nused for the capital cost calculation should also be elaborated.\nResponse: Thank you for your suggestions. We have added the description of the “raw\nmaterials”, \"energy\", \"facilities\" and \"labor costs\" in the method section in the\nmanuscript as (Line 514-519):\nThe raw materials refer to the solar evaporators, sodium alginate, PVA,\npoly(methyl methacrylate) and glass used. The energy cost refers to the manufacturing\nenergy of devices and fabrication of the solar evaporators. The facility cost refers to the\nnecessary parts to make the solar evaporation device work properly, which mainly\nincludes water containers and silicone tubes. The labor cost refers to the manufacturing\nfees of the solar evaporation device. The details of the capital cost estimation are\nincluded in the supplementary information (Table S3 and S4).\nIn the supplementary information we added the calculation processes of these costs\nas (Line 118-145):\nTable S3 The capital cost per area\nClassification Content Unit price Usage per area Cost per area/$ m-2\nSolar evaporator $0.070 kg-1 6.0 kg m-2 0.417\nSodium alginate $3.0 kg-1 0.0008 kg m-2 0.024\nRaw\nPVA $1.3 kg-1 0.0008 kg m-2 0.0104\nmaterials\nPoly(methyl methacrylate) $2.2 kg-1 18.17 kg m-2 40.44\nGlass $0.24 kg-1 14.43 kg m-2 3.44\nEnergy Electricity $0.088 kWh-1 3 kWh m-2 0.265\nWater container - $1 m-2 1.0\nFacilities\nSilicone tubes $2.55 kg-1 0.063 kg m-2 0.161\nTotal 45.7574\nThe raw material of the solar evaporator is sugarcane, and the cost is its price. The\nusage of the sugarcane (kg m-2) is estimated assuming that sugarcane has a density of\nwater (1g cm-3) as 1/(1000g/1g cm-3/0.6 cm)*10000 cm2 m-2, where 0.6 cm is the\nthickness of the solar evaporator (sugarcane).\nSodium alginate (SA), PVA usage was calculated as 200 mL m-2 * 0.04 g/100\nmL/1000 g kg-1, 200 mL m-2 is the amount of the cast solution used per square meter\nand 0.04 g/100 mL is the concentration of the SA and PVA solutions.\nPoly(methyl methacrylate) usage (kg m-2) was calculated considering a device\nwith a floor area of 4 m2, container height of 0.2 m, top cover tilting angle of 30°: (2\nm*2 m+0.2 m*2 m+(0.2 m+1.36 m)*2 m/2*2+1.36 m*2)* 0.006 m*1.19*103 kg m-3/4\nm2, where 2 m is the bottom side length, 1.36 m is the backboard height, 0.006 m is the\nthickness of the poly(methyl methacrylate) plate and 1.18*10-3 kg m-3 is the density.\nThe glass usage (kg m-2) is calculated considering a device with a floor area of 4\nm2, container height of 0.2 m, top cover tilting angle of 30°: 2.32 m*2 m* 0.005 m*2500\nkg m-3/4 m2, where 0.005 m is the thickness and 2500 kg m-3 is the density of glass.\nThe cost of electricity is estimated according to the manufacturing energy of\ndevices, and fabrication of the solar evaporators. The cost of facilities mainly includes\nwater containers and silicone tubes.\nTable S4 Labor cost estimation\nNational income levels Median monthly wage/$ Labor costs in terms of\nwages\nLow income 106.23 5.68\nLower-middle income 193.41 10.35\nUpper-middle income 390.00 20.86\nHigh income 2075.06 111.00\nWe take the median monthly income of China as a reference and estimate the labor\ncost to be $20.86 m-2 according to the actual manufacturing price for the solar\nevaporation devices. Then the labor cost is categorized into “low-income”, “lower-\nmiddle income”, “upper-middle income” and “high-income” countries\n(https://blogs.worldbank.org/search?keyword=country+classification). The labor costs\nwere obtained by normalizing the median monthly wage (https://ilostat.ilo.org/data/) of\nthese four categories of countries.\nReferences in the response letter:\nWater Harvesting. Adv Mater 31, e1806446 (2019).\nperformance and climate dependency of technologies for fresh water production\nfrom atmospheric water vapour. Environmental Science: Water Research &\nTechnology 6, 2016-2034 (2020).\nEfficient Solar Steam Generation from Zero to Three Dimensions. Adv. Funct.\nMater. 29 (2019).\nsolar steam generation. Environ. Sci.-Nano 9, 2264-2296 (2022).\nrandom forest models with limited sample size and different species traits. Fish\nRes 227 (2020).\nSelection in Random Forest Image Classification: A Case Study in Peatland\nEcosystem Mapping. Remote Sens-Basel 7, 8489-8515 (2015).\nValidation. Ieee T Knowl Data En 32, 1586-1594 (2020).\nUniversity of Technology, (1996).\nGilbert) 75-102 (Butterworth-Heinemann, 2017).\nGoal Targets on Drinking Water, Sanitation, and Hygiene. (World Bank, 2016)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have done their best to improve the quality of this manuscript, which can be published\nin the present form", "reviewer_2": "The authors have addressed my comments adequately and sufficiently.\nI believe they have also done so for the other reviewer's comments based on my assessment.\nI hope this paper gets into publication.", "reviewer_3": "In this revised manuscript, the authors have eliminated the misleading discussions on\ncondensation enhanced solar water evaporation (SWE) device, and provided the important details\nrelated to the outdoor field test and cost analysis. The reviewer appreciates the further discussion\non the water production performance of the 100-day operation, although it still needs further\ndiscussion.\n1. The authors claimed that the solar-to-water energy efficiency is positively related to the solar\nirradiance in the 100-day operation. However, it is hard to show the relationship based on the\nscattered data points in Fig. S7. More discussion about the correlation between the energy\nefficiency and solar irradiance is encouraged.\n2. The authors estimated a lifetime of 10 years for the SWE device according to the performance of\npoly (methyl methacrylate). However, it should be noted that an important but easy-to-fail\ncomponent of the device could be the coating for condensation, which accounts for the\nperformance enhancement of the SWE device in this study. How about the durability of this\ncondensation coating? Some more discussions and reference papers are encouraged here to\nfurther support the estimation of 10-year lifetime.\n3. The authors calculated the solar-to-water energy efficiency by taking both the solar energy and\nelectricity consumption into consideration. This can be unreasonable since they have different\nenergy grades. It is recommended to calculate an overall solar energy efficiency by assuming a\nphotovoltaic efficiency to convert the electricity consumption into solar energy consumption."}, "author_response": "Responses to the reviewers:\nReviewer #1 (Remarks to the Author):\nThe authors have done their best to improve the quality of this manuscript, which can\nbe published in the present form\nResponse: Thank you for your constructive suggestions and solid contributions to our\nmanuscript.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed my comments adequately and sufficiently.\nI believe they have also done so for the other reviewer's comments based on my\nassessment.\nI hope this paper gets into publication.\nResponse: Thank you for your constructive suggestions and solid contributions to our\nmanuscript.\nReviewer #3 (Remarks to the Author):\nIn this revised manuscript, the authors have eliminated the misleading discussions on\ncondensation enhanced solar water evaporation (SWE) device, and provided the\nimportant details related to the outdoor field test and cost analysis. The reviewer\nappreciates the further discussion on the water production performance of the 100-day\noperation, although it still needs further discussion.\n1. The authors claimed that the solar-to-water energy efficiency is positively related to\nthe solar irradiance in the 100-day operation. However, it is hard to show the\nrelationship based on the scattered data points in Fig. S7. More discussion about the\ncorrelation between the energy efficiency and solar irradiance is encouraged.\nResponse: Thank you for your suggestions. We have added two references in the\nmanuscript, which have also reported the positive relations between the solar\nevaporation efficiency and the solar irradiance:\n“The energy efficiency of case 4 shows no relation (Fig. S7a, c, Table S1, p>0.05,\nnot significant) to the day within the 100-day successive SMDW production test.\nInstead, the energy efficiency shows significant positive relations (Fig. S7b, c, Table\nS1, p<0.001) to the solar irradiance33, 34, demonstrating that the case 4 device operates\nstably with almost no deterioration.” (Manuscript line 146-150)\nReferences:\nThermal Steam Generation. Acs Nano 11, 5510-5518 (2017).\nMembrane for Highly Efficient Solar Thermal Generation of Clean Water. Acs Nano\n11, 5087-5093 (2017).\nIn addition, we have supplemented the SI (Supplementary document line 104-\n123)\n“By linear regression of energy efficiency and the day (Fig. S7a), the p-value of\nthe slope is 0.51, demonstrating that the linear relationship between them is not\nsignificant. In contrast, the p-value of the slope between the energy efficiency and the\nsolar irradiance is 0.001, demonstrating a significant linear relationship between them\n(Fig. S7b).\nMoreover, between-group comparisons (Day, Energy efficiency, and Solar\nirradiance) were performed using independent samples Kruskal-Wallis one-way\nanalysis of variance (ANOVA). The results showed that the correlation between the\nenergy efficiency and the day is not significant (p>0.9999) while the correlation\nbetween the energy efficiency and the solar irradiance is significant (p<0.0001, Fig.\nS7c).\nCorrelation analysis was further implemented to test for the effects of day and\nsolar irradiance on solar-water energy efficiency. The data regarding solar irradiance\nand solar-water energy efficiency were ln transformed before analysis to minimize the\nimpacts of the outliers. The results show that the solar-water energy efficiency has no\nsignificant relation with the pilot experiment day with p>0.05. On the contrary, the\nSpearman coefficient is 0.34 between the efficiency and the solar irradiance with\np<0.001.\nFrom the analysis above, it is concluded that solar-to-water energy efficiency is\npositively related to the solar irradiance while it stays stable within the 100-day\noperation.”\nFig. S7 The linear regression between solar-water energy efficiency and (a) Day\n(Slope=-0.03, p=0.51, ns), (b) Solar irradiance (Slope=3.6, p=0.001, ***). (c) Between-\ngroup comparisons of the day, energy efficiency, and solar irradiance (Day vs. Energy\nefficiency, p>0.05, ns; Energy efficiency vs. Solar irradiance, p<0.0001, ****).\nTable S1 The Spearman coefficients between solar-water energy efficiency and Day\nor Solar irradiance\nSpearman coefficients Day Solar irradiance\nSolar-water energy efficiency -0.078 0.34\np-value 0.44 4.7×10-4\n2. The authors estimated a lifetime of 10 years for the SWE device according to the\nperformance of poly (methyl methacrylate). However, it should be noted that an\nimportant but easy-to-fail component of the device could be the coating for\ncondensation, which accounts for the performance enhancement of the SWE device in\nthis study. How about the durability of this condensation coating? Some more\ndiscussions and reference papers are encouraged here to further support the estimation\nof 10-year lifetime.\nResponse: Thank you for your suggestions. We feel sorry for failing to make it clear\nthat the condensation coatings, solar evaporators are considered to be replaced every 2\nyears. Thus, we have revised the description in the section “Cost evaluation of SWE”:\n“The operation cost refers to the price of the consumable parts in the solar\nevaporation device that need to be replaced every 2 years. The maintenance cost refers\nto the repair of the whole solar evaporation device, whose lifespan is estimated as 10\nyears50,51. The maintenance costs were estimated as 3% of the capital costs per year and\ncomprised 30% of the capital costs (estimation details are included in the\nsupplementary document, section “Cost estimation”).” (Manuscript line 534-538)\nWe have added the detailed operation and maintenance estimation in the\nsupplementary information (Supplementary document line 213-261)\n“Operation and maintenance cost:\nOur devices mainly comprise acrylic containers, carbon solar evaporators, top\ncover glass, hydrogel-based condensation coatings, and connecting silicone tubes.\nThe operation cost refers to the total price of the parts in the solar evaporation\ndevice that need to be replaced after a certain period. It mainly includes solar\nevaporators and hydrogel-based condensation coatings.\nThe solar absorber used in our study is inorganic biochar, which is stable in the\nenvironment. According to previous reports, solar stills composed of typical inorganic\ncarbon-based black paint exhibit a long lifespan ranging from 2-10 years1-3. In addition,\nduring our 100-day outdoor test, the solar absorbers showed no observable deterioration.\nThus, its lifespan is estimated at 2 years.\nThe coating for accelerated condensation is composed mainly of PVA fibers. PVA\nis chemically stable and widely used in coatings and fibers. As previously reported,\nPVA coatings are stable (remain hydrophilic and antifogging) after 2-7 months of\nexposure to a hot and humid environment or daily use4, 5. Moreover, PVA has a strong\nbond with the matrix, exhibiting resistance to an alkaline environment. They also\nestimated that the tensile strength of PVA fiber could be preserved after even 60 years\nof ultraviolet irradiance in a hot environment6. In addition, the coating used in our\noutdoor test shows no obvious deterioration after 100 days, so its lifespan is also\nestimated at 2 years for simplification of calculations.\nTherefore, from Table S3, the solar evaporator, sodium alginate, PVA, and the\nelectricity used for fabrication is a total of $0.72 m-2, which is considered as consumable\nmaterial and should be replaced. The operation cost was comprised of the substitution\nof the solar evaporators and condensation coatings and the auxiliary software cost (2%\nof the materials cost).\nThe maintenance cost refers to the repair of the whole solar evaporation device\n(whole capital cost). For the whole devices mainly made of Poly(methyl methacrylate)\n(PMMA), its lifetime is estimated as 10 years considering the property of the PMMA7,\n8. Therefore, besides the operation cost to replace the solar evaporators and the\ncondensation coatings, the maintenance costs were estimated at 3% of the capital costs\nper year and comprised 30% of the capital costs to fix the problems that the whole solar\nevaporation device may encounter.”\nAdded references:\nEnviron. Sci. 11, 1510-1519 (2018).\nstill coated with TiO2 nano black paint. J Clean Prod 213, 185-191 (2019).\nsolar still with enhanced evaporation methods – Comprehensive study. Sol Energy 203,\n164-174 (2020).\nantifogging coating with self-healing performance. J Coat Technol Res (2024).\npollution and intensive UV irradiation. Mater Design 194 (2020).\nDurability Design with Strain-Hardening Cement-Based Composites (SHCC): State-\nof-the-Art Report of the RILEM Technical Committee 240-FDS (eds Gideon P. A.\nG. van Zijl & Volker Slowik) 59-78 (Springer Netherlands, 2017).\nUniversity of Technology, (1996).\nGilbert) 75-102 (Butterworth-Heinemann, 2017).\n3. The authors calculated the solar-to-water energy efficiency by taking both the solar\nenergy and electricity consumption into consideration. This can be unreasonable since\nthey have different energy grades. It is recommended to calculate an overall solar\nenergy efficiency by assuming a photovoltaic efficiency to convert the electricity\nconsumption into solar energy consumption.\nResponse: Thank you for your suggestions. We agree that solar heat and solar electricity\nhave different energy grades. Thus, we have revised the energy efficiency calculation\nmethod (Manuscript lines 415-423)\n“The solar-to-water energy efficiency was calculated as the ratio between the heat\nof the generated vapor and the consumed energy45:\nṁ×h\nfg\nη =\nsolar-water q +q\nsolar solar-electricity\nWhere, ṁ is mass flow of the SMDW yield (kg m-2), h is the latent heat (kJ\nfg\nkg-1), q is the incident solar intensity for heat conversion (kJ m-2) and q is\nsolar solar-electricity\nthe incident solar intensity for electricity generation (kJ m-2). The power of the vapor\npump is 5 W and works all day to pump the headspace water vapor in a work area of\n17×17 cm2. Considering that the photovoltaic cells available in the market have an\naverage energy conversion efficiency of 20%46, the incident sunlight was estimated as\nelectricity consumption/20%.”\nCorrespondingly, Fig. 2d has been revised and the analysis are updated as follows:\n“Comparatively, pumping out the vapor through the condensing tube elevates the\nSMDW yield of cases 3 and 5, but their energy efficiency only ranges from 0.6–14.3%\nand 1.6–15.2% by taking the solar energy used for the electricity consumption of the\nvapor pump into consideration, respectively, which is even inferior to the case 2 device\nwithout enhanced condensation.” (Manuscript lines 154-158)\nRevised Fig. 2d Statistical distribution of the solar energy utilization efficiency of all\ncases.\nAdded reference:\n46. Morales Pedraza, J. in Non-Conventional Energy in North America (ed Jorge\nMorales Pedraza) 137-174 (Elsevier, 2022)."}, {"round": 3, "reviewer_comments": {"reviewer_3": "All my comments have been well addressed. I recommend its publication in current form."}, "author_response": "Response: Thank you for your efforts and suggestions."}]

Decision letter