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Fishing vessels have legitimate reasons to turn off their position-tracking systems — but there are some suspicious reasons, too.Credit: Anthony Wallace/AFP/Getty

When fishing vessels hide their locations, they sometimes reveal a wealth of information. Gaps in tracking data can hint at illegal activity, finds a modelling study1.Some ships carry automatic identification systems (AIS), which pinpoint their locations and help to prevent collisions, but can be turned off manually. Researchers studied gaps in the tracking data to identify hotspots where fishing vessels frequently disabled their devices on purpose — and to explore the possible reasons. The findings suggest that vessels hid up to 6% of their activity — more than 4.9 million hours between 2017 and 2019. Some of these gaps could mask illegal fishing, finds the study, which was published in Science Advances this month..The study uses holes in tracking data “to tell us more about what we’re not seeing, what we’re missing”, says Juan Mayorga, a marine data scientist based in Santa Barbara, California, who is part of the National Geographic Society’s Pristine Seas project. “That is a really valuable contribution.”Expensive problemIllegal, unreported and unregulated fishing costs the global economy up to US$25 billion each year. It is also detrimental to marine life, and some evidence suggests that it is linked to human-rights violations such as people trafficking. Heather Welch, a spatial ecologist at the University of California, Santa Cruz, and her colleagues analysed more than 3.7 billion signals from vessels, sent over three years and recorded in the Global Fishing Watch AIS data set. The team used a model to distinguish between gaps caused by vessels intentionally turning off their AIS and those that were due to technical issues. Gaps of 12 hours or more when ships were at least 50 nautical miles from shore in areas with adequate signal reception were suspected to be intentional disabling.

Source: Ref 1.

The team found that 82% of time lost to AIS disabling happened on ships flagged from Spain, the United States, Taiwan and the Chinese mainland (see ‘Flag of origin’). Although most vessels that use AIS come from middle- and upper-income countries, so the data are biased towards those countries, the study says. “AIS is not feasible for a lot of countries globally at the moment,” says Claire Collins, a marine social scientist at the Zoological Society of London.There are many reasons vessels intentionally turn off their AIS, says Welch, and not all of them are nefarious. For instance, crews might hide their location in areas where pirates are a threat, or might obscure their position from competitors when fishing in a bountiful area. More iniquitous reasons to hide a ship’s location include trying to mask illegal fishing or unauthorized transshipment — transfers of cargo between ships at sea — she says.The team used another model to investigate what was behind the intentional AIS signal gaps, looking at factors such as how productive an area is for fishing, the risk of piracy and the level of transshipment activity. The results indicate locations in which the signal gaps are potentially nefarious, but they cannot definitively say whether these gaps hide illegal activity, says Welch.HotspotsThe model revealed 4 hotspots for intentional AIS disabling: 16% of gaps occurred next to Argentina’s exclusive economic zone, 13% in the Northwest Pacific Ocean, 8% adjacent to the exclusive economic zones of West African nations and 3% near Alaska. Apart from Alaska, these hotspots are already regions of concern for illegal, unreported and unregulated fishing. They produce a lot of fish and have limited management, partially because of their locations in the high seas. Signal gaps near exclusive economic zones indicate that vessels could be hiding that they are crossing boundaries without authorization to fish in restricted areas, says Welch. “If they were allowed to go in that zone, why would they disable their AIS?” she says.Drifting longlines were the fishing vessels found to disable their AIS most often, followed by tuna purse seines (see ‘Out of sight’). Intentional AIS disabling events were also common near transshipment hotspots. Offloading catch at sea helps to reduce costs, but past research has linked it to human trafficking and slipping illegal catch on to the market.

Source: Ref 1.

The research is a good way to start exploring what AIS-disabling data can expose, and could help researchers to conduct finer-scale studies in the future, says Collins. “It’s a really important study.”Mayorga agrees that the data will aid fishery managers in understanding the magnitude and patterns of illegal fishing, helping them to zero in on specific problematic regions and improve enforcement of laws at sea. More

Figure 4 shows a sample of the seasonal cycle and spatial distribution of SWE over HUC2 basins and the entire WUS domain in WY 2019. No SWE or snow depth measurements are assimilated in deriving the WUS–SR dataset. Thus, in situ SWE and snow depth measurements, and ASO SWE and snow depth estimates are used as independent verification datasets. Landsat fSCA measurements are assimilated into the snow reanalysis framework assuming a measurement error (standard deviation) of 10%34. Though Landsat fSCA cannot be used for independent verification, the WUS–SR posterior fSCA estimates, which are fitted to these measurements using a likelihood function, are expected to have comparable bulk error. The snow reanalysis framework has been successfully applied previously to generate datasets over the Sierra Nevada, Andes, and High Mountain Asia33,50,52.Fig. 4Illustrative results from the WUS–SR SWE estimates in WY 2019. (a) Seasonal cycle of SWE volume (km3) integrated over HUC2 basins. (b) Spatial distribution of SWE (meters) over part of the Sierra Nevada on March 1st, WY 2019. (c) Spatial distribution of WUS SWE (meters) on March 1st, 2019. The boxed area in (c) represents that shown in (b).Full size imageVerification with in situ dataIn this section, grid-averaged reanalysis SWE and snow depth are compared with point-scale in situ measurements. It should be acknowledged a priori that there are inevitable representativeness issues in the comparison between point-scale in situ data and grid-averaged snow reanalysis data. The WUS–SR estimates are modeled with assumed sub-grid heterogeneity within each ~500 m grid cell (which is modeled via a lognormal distribution) meant to account for the complex sub-grid variations in terrain (elevation, slope, aspect), forest cover, and meteorological forcings. Given that in situ stations are often sited in non-representative regions of a grid cell (i.e., in sheltered flat forest clearings), it is unlikely that the grid-averaged SWE/snow depth (spanning ~ 250,000 m2) should match the point-scale in situ SWE/snow depth (spanning ~10 m2). Nevertheless, in situ measurements, from the SNOTEL and CA Department of Water Resources (CADWR) networks, represent the best available data that covers much of the WUS and extends back several decades. While not expected to match each other, the verification herein is meant to illustrate consistency between the in situ measurements and WUS–SR estimates.Peak SWE comparison with in situ dataIn situ SWE measurements from WY 1985 to 2021 are taken from 1) the SNOTEL network (https://www.wcc.nrcs.usda.gov/snow/) managed by the U.S. Natural Resources Conservation Service (NRCS), and 2) CADWR (https://cdec.water.ca.gov/dynamicapp/staSearch from sensor type: “SNO ADJ (82)”), collections of automated snow pillows in the WUS. For in situ verification, we pair each in situ site with the closest snow reanalysis grid based on the geolocation of these two datasets. The precision of in situ coordinate values varies from 0.000001° (1 km). Considering the potential for geolocation mismatch, the nine nearest pixels32,33,55 are additionally used to compare in situ and WUS–SR peak SWE. In this latter approach, the differences between in situ peak SWE and the neighboring WUS–SR grid cell peak SWE with the smallest difference among the nine nearest snow reanalysis grids are used. To compare the SWE on the same day, peak SWE day determined by in situ SWE is used to extract peak SWE from both datasets throughout the paper.Figure 5 presents the density scatter plots comparing in situ peak SWE values against collocated grid-cell posterior peak SWE values. Peak SWE values less than 1 cm are screened out from the comparison. In total, 928 in situ sites are used in the comparison with the WUS–SR SWE estimates. To understand the performance of the WUS–SR dataset across different regimes in the WUS, verification is conducted for each HUC2 basin. The comparison is quantified using correlation coefficient (R), mean difference (MD), and root mean square difference (RMSD). Table 5 summarizes the number of total site-years, and statistics for both prior and posterior reanalysis SWE against in situ SWE within each HUC2 basin and over the WUS.Fig. 5Density scatter plot of in situ (snow pillow) peak SWE and collocated posterior (grid-average) peak SWE grouped by HUC2 basins over WYs 1985 to 2021. The solid black line is the 1:1 line. The correlation coefficient (R), mean difference (MD), and root mean square difference (RMSD) are shown for each HUC2 basin. In situ data with peak SWE values greater than 1 cm are included in the comparison.Full size imageTable 5 Number of in situ sites and comparison metrics between in situ (snow pillow) peak SWE and collocated grid-averaged snow reanalysis prior and posterior (post.) peak SWE grouped by HUC2 basins.Full size tableCompared with the performance of the prior peak SWE estimates (i.e., not constrained by Landsat fSCA), posterior SWE estimates show a better correlation (higher R) with less bias and random error (lower MD and RMSD) than the prior SWE over most of the HUC2 basins. Posterior SWE in CA has the highest correlation against in situ SWE (R = 0.82). The correlations with in situ SWE over the entire WUS are improved from 0.74 (prior) to 0.77 (posterior). Posterior peak SWE in UCRB has lower bias and uncertainty compared against in situ data with a relatively small MD of 0.06 m in absolute value (reduced by 62% from prior MD) and RMSD of 0.19 m (reduced by 27%). Over the WUS, in situ peak SWE is (on average) larger than the WUS–SR peak SWE (negative MD). Sub-grid topographic variability, snow-forest interactions, and wind-driven snow redistribution may all cause differences seen between grid-averaged peak SWE and point-scale in situ peak SWE. The statistics for PN indicate comparable correlation of in situ and both prior and posterior snow reanalysis, however the MD and RMSD do not get improved from posterior to prior. Fewer cloud-free fSCA measurements are available in PN, which limits the improvement of snow reanalysis SWE via data assimilation.To acknowledge the potential geolocation mismatch, Fig. 6 provides verification of in situ peak SWE and posterior reanalysis peak SWE using an approach comparing to the best match among the nine nearest pixels. The WUS-wide correlation coefficient (R), MD and RMSD of posterior peak SWE and in situ peak SWE is 0.91, −0.08 m, 0.18 m, respectively. Compared to the approach used in Fig. 5, the posterior reanalysis peak SWE in Fig. 6 (as expected) is more correlated with in situ peak SWE (R values above 0.9), and has lower MD ( More

Evaporation structure design and fabricationFor conventional salt-rejection solar evaporation systems, water evaporation is confined to the solar absorber surface, and the salt backflow is accompanied by an undesired heat dissipation from the solar absorber to bulk water, thus resulting in a low evaporation rate. This limitation can be solved to a considerable extent by our 3D evaporator. As illustrated in Fig. 1a, the top surface of our evaporator is a solar absorber layer used for light-to-heat conversion to generate vapour. Beneath the solar absorber are a number of vertically aligned MTBs connecting the saline water to the solar absorber. MTBs have hydrophilic microchannels that can pump saline water to the solar absorber via a capillary force. Furthermore, excessive salt can flow back into the bulk water through these brine-filled microchannels via diffusion and convection (Fig. 1b-1). The adequate mass transfer via a high density of MTBs ensures a continuous water supply and an efficient salt backflow, thus enabling a unique salt rejection capability. Unlike conventional salt-rejection systems, where the heat conducted from the solar absorber to the bulk water is simply dissipated and considered “wasted,” the MTBs can efficiently recover this conductive heat to generate additional vapour from the brine flowing through their microchannels (Fig. 1b-2). The microchannels within the MTBs and macrochannels between the spaced MTBs together form a highly open structure that allows the generated vapour to be easily released from the MTB surfaces in all directions. We envision that by optimizing the MTB height, conductive heat can be largely confined in them for vapour generation, thereby significantly improving the water evaporation efficiency.Fig. 1: Design and fabrication of the 3D salt-rejection evaporation structure.a Schematic of the 3D salt-rejection solar evaporator. b Working principle includes salt rejection and evaporation enhancement. c UV–Vis–NIR spectra of the GFM, CNT-coated GFM, and standard solar irradiation spectrum of AM 1.5 G. d SEM image of the CNT-coated GFM surface. e SEM image of the GFM. f Image of the water drop hanging above the GFM and the moment it touches the GFM surface. g Anti-gravity transport of water along a GFM. h 3D salt-rejection evaporator prototype. i Schematic illustration of the fabricating process of the evaporator.Full size imageWe achieved the designed structure by fabricating the top solar absorber layer by loading carbon nanotubes (CNTs) with a diameter of about one hundred nanometres on a glass fibre membrane (GFM). The solar absorption of wet CNT-coated GFM can reach ~96% (Fig. 1c) because of the porous fibrous light-trapping structure (Fig. 1d) and the inherent black property of the CNT27. Considering their abundant hydrophilic microchannels formed by intertwined glass fibres (Fig. 1e), the GFMs were also selected for use as MTBs. A GFM can immediately absorb a water droplet upon touching it because of its high affinity to water (Fig. 1f). Moreover, vertically aligned GFMs (i.e., MTBs) can pump water to 25 cm height in 60 min, demonstrating its strong capillary force for water transfer (Fig. 1g). A complete evaporation system was fabricated by assembling a number of MTBs and the solar absorber in a plastic frame (Fig. 1h, 1i and Fig. S1).Salt rejection capabilityTo avoid salt crystallization, excess salt must be efficiently transported back to maintain the top surface salinity below the saturation point. In this system, salt can be rejected via diffusion and convection through brine-filled microchannels under the driving force of the concentration gradient (osmosis) and gravity25. Its mass flow rate ((J)) can be described by the diffusion–convection equation as follows28,29:$$J={J}_{{diff}}+{J}_{{conv}}={nA}varepsilon ({k}_{d}({C}_{{evp}}-{C}_{0})/l+{k}_{c}({rho }_{{evp}}-{rho }_{0}))$$
(1)
where ({J}_{{diff}}) and ({J}_{{conv}}) are the mass flow rate caused by diffusion and convection, respectively; (n) is the number of MTBs; (A), (varepsilon), and (l) are the cross-section area, porosity, and height of the MTBs, respectively; ({k}_{d}) and ({k}_{c}) are the diffusion and average convective coefficients of salt, respectively; ({C}_{{evp}}) and ({C}_{0}) are the salt concentrations on the evaporation surface and in the bulk saline water, respectively; and ({rho }_{{evp}}) and ({rho }_{0}) are the salt solution densities on the evaporation surface and in the bulk saline water, respectively.In Eq. (1), the mass transport rate is proportional to the bridge number (n). We validated this relation by fabricating MTB structures with different bridge numbers ranging from 2 to 32 [Fig. 2a, cross-section area ((A)): ~0.135 cm2; height ((l)): 3 cm; porosity ((varepsilon)): ~65%] and evaluating their evaporation performance using high-salinity water (10 wt.% NaCl). The evaluation was performed under 1 sun illumination for 12 h. Figure 2b shows that salt crystals massively accumulated on the 2-bridge evaporator surface because of insufficient mass transfer. This salt accumulation was mitigated with increase in the bridge number. For the evaporator containing 32 MTBs, no salt crystals were observed on the surface after the 12 h operation (Fig. 2b). At an insufficient number of MTBs (≤16), the evaporation rate gradually decreased as the vapour generation progressed because of the increased evaporation surface salinity (Fig. 2c, see the corresponding mass change curves in Fig. S2). In contrast, with sufficient MTBs (e.g., 32 bridges), the excess salt can be efficiently rejected to maintain the evaporation surface at a relatively low salinity. Remarkably, the evaporation rate of the 32-bridge evaporator was ~1.44 kg/m2/h without degradation during the 12 h operation.Fig. 2: Salt rejection performance.a Photograph of evaporators with various bridge numbers (bridg height: 3 cm). b Photographic recordings of the salt accumulation on the 3D evaporators with different MTB numbers. c Evaporation rate variations of evaporators during long-term operations. d Photos of salt redissolving from the surface of a 32-bridge evaporator.Full size imageSubsequently, we performed a complementary experiment to more intuitively demonstrate the salt backflow introduced by the 32-bridge evaporator. In this experiment, the evaporator was placed in a high-concentration saline water (10 wt.% NaCl solution) and exposed to 1 sun illumination, and 1 g of NaCl salt was added on its surface (upper panel, Fig. 2d). It was seen that during vapour generation, the added salt was gradually dissolved and completely removed in 11 h (lower panel, Fig. 2d; more details in Fig. S3). This experiment demonstrated that the salt backflow rate of the 32-bridge evaporator in the 10 wt.% NaCl solution was higher than the salt generation rate, thus confirming the salt rejection feature of the proposed MTB architecture. We further increased the brine salinity to test the maximum applicable salt concentration of this evaporator. Because the effects of diffusion and convection backflow decreased as the salinity (i.e., ({C}_{0}) and ({rho }_{0})) increased, salt started to crystallize at the edges of the solar absorber after 12 h operation when 14 wt.% NaCl solution was used for the test (Fig. S4). Based on the corresponding evaporation rate, the salt backflow along the MTBs was calculated as ~1.1 g/cm2/h. Interestingly, this unique mass transport feature is intertwined with its heat transport feature, as demonstrated in the subsequent section.Heat managementWe fabricated 32-bridge evaporators with different bridge heights (Fig. 3a) and evaluated their evaporation performance. Under dark conditions, the evaporator without MTBs (i.e., bridge height: 0 cm) exhibited a natural evaporation rate of 0.15 kg/m2/h, which became more pronounced with the incorporation of MTBs due to the increased surface area (Fig. S5). Specifically, it linearly increased by ~0.04 kg/m2/h for every 1 cm increase in the MTB height. Under 1 sun illumination, the evaporation rate of the evaporator without MTBs was only 0.99 kg/m2/h because of the massive conductive heat dissipation to the bulk water (Fig. 3b, see the mass change curves in Fig. S6). The MTB usage considerably promoted solar evaporation. The evaporation rate increased to 1.58–1.73 kg/m2/h when the bridge height reached 2–5 cm (Fig. 3b). These values are even higher than the theoretical upper limit for solar evaporation (~1.44 kg/m2/h, Supplementary Note 1 and Fig. S7), which can be attributed to the natural evaporation contribution (Fig. S8). When the MTB height exceeded 3 cm, the evaporation rate increased by ~0.04 kg/m2/h for every 1 cm increase in MTB height (Fig. 3b), which was consistent with the result obtained under dark conditions. This consistency suggests that the 3 cm height is sufficient for the MTB structure to maximize solar evaporation (note that additional increase in MTB height only increases natural evaporation). To reveal the mechanism of this observation, we analyzed the heat transport in this unique architecture.Fig. 3: Evaporation performance, heat management, and stability evaluation.a Photograph of evaporators with various bridge heights (bridge number: 32). b Evaporation rate of evaporators with different MTB heights under 1 sun illumination (error bar type: standard deviation). c Internal temperature variation at different distances from the solar absorber. d Demonstration of the bulk water temperature after 3 h operation with different evaporators. e Photograph of the enclosed evaporator after 3 h evaporation. f Mass change curves and evaporation rate during the cycling experiment.Full size imageThe energy loss channels for this evaporation system primarily include conductive heat loss into the bulk water (({P}_{{cond}.})), radiative heat loss (({P}_{r{ad}.})), and convective heat loss to the environment (({P}_{{convec}.})). Therefore, the power flux available for evaporation (({P}_{{evp}})) can be described as follows16:$${P}_{{evp}}={P}_{{solar}}-{P}_{{cond}.}-{P}_{{rad}.}-{P}_{{convec}.}$$
(2)
where the solar energy input ({P}_{{solar}}={{{{{rm{alpha }}}}}}{C}_{{opt}}{q}_{i}); ({{{{{rm{alpha }}}}}}) is the light absorption coefficient; ({C}_{{opt}}) is the optical concentration; and ({q}_{i}) is the direct solar illumination. The conductive heat flux ({P}_{{cond}.}=k({T}_{{sa}}-{T}_{{bw}})/l), where (k) is the thermal conductivity; ({T}_{{sa}}) and ({T}_{{bw}}) are the temperatures of the solar absorber and the bulk water, respectively; and (l) is the heat conduction path referring to the MTB height in our model. The radiative heat flux ({P}_{{rad}.}=varepsilon {{{{{rm{sigma }}}}}}({{T}_{1}}^{4}-{{T}_{2}}^{4})), while the convective heat flux ({P}_{{convec}.}=hleft({T}_{1}-{T}_{2}right),) (varepsilon) is the optical emission, ({{{{{rm{sigma }}}}}}) is the Stefan–Boltzmann constant, (h) is the convection heat transfer coefficient, and ({T}_{1}) and ({T}_{2}) are the temperatures of the evaporator and environment, respectively.The energy loss caused by the heat transfer from the top surface to the bulk water (i.e., ({P}_{{cond}.})) can be minimized by increasing the MTB height (i.e., (l)) to confine the conductive heat within the MTB structure. This effect was visualized using infrared imaging to display the temperature gradients along MTBs with different heights. The results showed that the temperature at the bottom of the evaporator was similar to the ambient temperature when the MTB height reached or exceeded 3 cm (Fig. S9). This temperature distribution agreed well with the simulation modelled by COMSOL (Fig. S10). To obtain more insights into the heat transport in the architecture, we carefully recorded the internal temperature variation at different distances to the solar absorber under solar illumination. The results showed that the temperature stabilized after 60 min, when the internal temperature at 3 cm to the solar absorber was similar to that of the surrounding environment (Fig. 3c), indicating that the conductive heat was completely confined in the top 3 cm of the MTB structure. This confinement effect was also demonstrated by the temperature change of the bulk water (Fig. 3d): for the evaporator without MTBs, the bulk water temperature increased from ~21 to ~26.2 °C after a 3 h operation due to the continuous heat input (left panel); for the evaporator with 3-cm MTBs, however, the bulk water temperature was maintained at room temperature (~21.3 °C) (right panel), thus confirming the suppression of heat dissipation into the bulk water.Importantly, the confined heat energy can be exploited to generate additional vapour from the MTB surfaces, which can be efficiently released via the highly open interbridge spaces. To reveal this additional vapour generation from the vertical surfaces of MTBs, we used an evaporator having 32 MTBs (3 cm high) to perform a control experiment. In this experiment, the evaporator body was enclosed with an airtight polypropylene film, thus leaving only the upper surface exposed to the open space for vapour release (Fig. S11). After a 3 h operation, many water droplets condensed on the inner film surface, thus confirming that the MTBs released vapour (Fig. 3e). Compared to the completely open evaporator, the evaporation rate of the partially enclosed system decreased by ~31% (Fig. S12), demonstrating the importance of the open-channel design for enhanced interfacial evaporation.Furthermore, we performed a cycling experiment to evaluate the evaporator stability. In each cycle, the evaporator ran for 12 h under 1 sun illumination and in a dark environment for another 12 h to simulate day and night alternation. Figure 3f shows that during this long-term test (with 10 wt.% NaCl solution), the mass change of the NaCl solution in each cycle linearly evolved and the evaporation rate stabilized at ~1.44 kg/m2/h. No performance degradation was observed after a seven-day cycling experiment.Compared with the previously reported salt-rejection evaporators (evaporation rate: from 1.24 to 1.28 kg/m2/h for 10 wt.% NaCl solution)9,26,30, our evaporator demonstrated a higher evaporation rate under similar conditions due to the heat confinement effect and the natural evaporation contribution. However, high evaporation efficiency alone is not sufficient for water production applications. If the evaporated moisture is not collected, it can only be considered as a pollutant to the environment considering that it has the greatest greenhouse effect among various components in the atmosphere31. Water collection that is equally important as vapour generation has been largely ignored in many previous studies on salt-rejection evaporators.Therefore, we enclosed the evaporator with a transparent cover made of polymethyl methacrylate (PMMA) plates, creating a system that can produce water by condensing the evaporated moisture, and investigated the effects of bridge number and bridge height on the water production capacity of this system (Fig. S13a). When the bridge height was fixed at 3 cm, the amount of collected water increased with the number of bridges (Fig. S13b), which is consistent with the observation in the open system, confirming that the enhanced salt backflow facilitates water evaporation. When the bridge number was fixed at 32, the amount of collected water increased with the bridge height and reached the maximum at 3 cm, while further increasing the bridge height did not produce more water (Fig. S13c). This result is consistent with the conclusion above that 3 cm is sufficient to confine the conductive heat while further increasing bridge height only increases natural evaporation that does not contribute to water production. According to the three-hour test results, the water production rate of the enclosed evaporator in the optimal configuration (32 bridges; 3 cm high) is calculated to ∼0.68 kg/m2/h (Fig. S13).We also investigated the water generation performance of the enclosed system under different salinity conditions using NaCl solutions (3.5−20 wt.%). The results showed that the water production efficiency monotonically decreased from ~0.73 kg/m2/h for 3.5 wt.% NaCl solution to ~0.63 kg/m2/h for 20 wt.% NaCl solution (Fig. S14a). The relatively low water production efficiency associated with the high-salinity brines is mainly due to their low saturated vapour pressure, partly due to the decreased photothermic conversion efficiency caused by salt precipitation. For instance, when using brine containing 20 wt.% NaCl, salt precipitation emerged at the periphery of the evaporator after three hours of testing (Fig. S14b).Field testsAs per the recently announced “best practice for solar water production”32, the daily water yield is an important evaluation criterion that deserves additional consideration in practical implementations. Therefore, we prepared closed system based on the MTB structure and measured their water generation capacity under practical outdoor conditions.Rooftop experimentThe fabricated solar-driven water generation system has a 15 × 26 cm2 evaporator area (see Fig. S15). We first tested the system on the rooftop in KAUST, Thuwal, Saudi Arabia (Fig. S16). In this experiment, we employed the discharged water from an RO system of the KAUST Seawater Desalination Plant as the source water (salinity: ~8.7%). Our daily evaluation started at 8:00 and ended at 17:00. As shown in Fig. 4a, the evaporator surface was heated by solar light to a temperature 4–15 °C higher than the environment. However, the temperature at the bridge bottom was almost the same as the environment temperature, indicating that the conductive heat was confined, with only a small amount transferred to the bulk water. Consequently, saline water can be efficiently evaporated and condensed at the cover surface for the water collection. Figure 4b and Supplementary Movie 1 illustrate the relevant details. The total collected water was ~175 ml, of which ~110 ml flowed in the graduated cylinder, and ~65 ml was retained in the PMMA cover. Based on the evaporator area (390 cm2), the daily water productivity was calculated as ~5.0 L/m2. We measured the ion contentions of our water samples to evaluate the water quality. Compared with the discharged water from the RO plant, the ion concentration of condensed water was reduced by at least four orders of magnitude, thus fully meeting the WHO drinking water requirements (Fig. 4c).Fig. 4: Field tests.a Real-time temperature variation of the solar absorber, environment, bottom of bridges and bulk water, and solar flux from 8:00 to 17:00 on Apr. 11, 2022. b Timelapse photos of the collected water in the graduated cylinder from 8:00 to 17:00. c Ion concentration in the effluent water collected from the RO facility and collected freshwater from our system. d Daily water generation, solar insolation, and solar–water collection efficiency from Apr. 7 to 11, 2022. e Photograph of the evaporator after five-day operation. f Photograph of the floating system for the ocean test. g Schematic illustration of the structure of the floating system. h Daily water collection, solar insolation, and solar–water efficiency during the ocean test from Apr. 17 to 21, 2022.Full size imageWe calculated its practical solar–water collection efficiency of the system, ({eta }_{{prac}}), using Eq. (3):$${eta }_{{prac}}={m}_{{cond}}{h}_{{lv}}/left({A}_{{evp}}int {q}_{{solar}}left(tright){dt}right)$$
(3)
where ({m}_{{cond}}) is the daily water collection amount; ({h}_{{lv}}) is the total enthalpy of the liquid–vapour phase transition; ({A}_{{evp}}) is the evaporator area; and ({q}_{{solar}}) is the time-dependent solar flux. Benefiting from the highly efficient vapour generation, the overall solar–water collection efficiency of our system reached ~41.6%, representing a considerable improvement compared to the previously reported salt-rejection solar evaporation systems (e.g., maximum efficiency of a rooftop system: ~24%25). We performed a continuous test from Apr. 7 to Apr. 11, 2022 to evaluate the performance stability (Fig. 4d). The daily water collection rate fluctuated in the range of 4.7–5.2 L/m2 depending on the specific solar insolation of the day. The corresponding solar–water collection efficiency was 39%–42%. Remarkably, no salt accumulation was observed during this five-day outdoor operation (Fig. 4e). These results demonstrate the potential of the fabricated evaporator to extract freshwater from the wastewater discharged by RO plants.Floating testAfter the 5-day rooftop experiment, the same MTB-based evaporation system was tested in a floating configuration in the Red Sea (salt content: ~4.3%) to demonstrate its potential for practical seawater desalination (Fig. 4f, g). The test started and ended at 8:00 and 17:00, respectively, each day and lasted for five days from Apr. 17 to Apr. 21, 2022. As shown in Fig. 4h, the daily freshwater productivity ranged from 5.0 to 5.8 L/m2 with a stable solar–water collection efficiency of 42%–45%, which was consistent with the rooftop test. This freshwater productivity was approximately two times higher than the previous record of the salt-rejection solar evaporator (~2.5 L/m2 per day)25. The field test demonstrated a high-performance solar evaporator that will help in disaster relief or strengthen the resilience of individuals living on boats and coastal areas. More

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