A Techno-Economic Analysis of Solar-Driven Atmospheric Water Harvesting

2020 ◽  
pp. 1-35
Author(s):  
Nathan Siegel ◽  
Ben Conser
Keyword(s):  
State Of The Art ◽  
System Level ◽  
Water Model ◽  
Water Harvesting ◽  
Atmospheric Conditions ◽  
Atmospheric Water ◽  
Atmospheric Humidity ◽  
Energy Flows ◽  
Solar Electricity ◽  
Latent Heat Of Vaporization

Abstract Water may be produced from atmospheric humidity anywhere on Earth; however, current approaches are energy intensive and costly, thus limiting the deployment of atmospheric water harvesting (AWH) technologies. A system level thermodynamic model of several AWH pathways is presented to elucidate the important energy flows in these processes as a means to reducing the energy required to produce a unit of water. Model results show that fresh water may be produced from humid air via processes driven solely with solar electricity in an arid climate with an energy input between 116 kWhe/m3 and 1021 kWhe/m3, depending on atmospheric conditions and processing configuration. We describe a novel, desiccant-based AWH approach in which the latent heat of vaporization is internally recovered resulting in a significant reduction in energy requirements relative to the state of the art. Finally, a parametric model of a desiccant-based AWH system is used to estimate the minimum levelized cost of water (LCOW) via solar-driven AWH at 6.5 $/m3 when both latent and sensible energy are recovered internally.

A Techno-Economic Analysis of Solar-Driven Atmospheric Water Harvesting

2020 ◽  
Author(s):  
Nathan P. Siegel ◽  
Ben Conser
Keyword(s):  
State Of The Art ◽  
System Level ◽  
Water Model ◽  
Water Harvesting ◽  
Atmospheric Conditions ◽  
Atmospheric Water ◽  
Atmospheric Humidity ◽  
Energy Flows ◽  
Solar Electricity ◽  
Latent Heat Of Vaporization

Abstract Water may be produced from atmospheric humidity anywhere on Earth; however, current approaches are energy intensive and costly, thus limiting the deployment of atmospheric water harvesting (AWH) technologies. A system level thermodynamic model of several AWH pathways is presented to elucidate the important energy flows in these processes as a means to reducing the energy required to produce a unit of water. Model results show that fresh water may be produced from humid air via processes driven solely with solar electricity in an arid climate with an energy input between 158 kWhe/m3 and 1021 kWhe/m3, depending on atmospheric conditions and processing configuration. We describe a novel, desiccant-based AWH approach in which the latent heat of vaporization is internally recovered resulting in a significant reduction in energy requirements relative to the state of the art. Finally, a parametric model of a desiccant-based AWH system is used to estimate the minimum levelized cost of water (LCOW) via solar-driven AWH at 6.5 $/m3 when both latent and sensible energy are recovered internally.

The Three-Cornered Hat Method for Estimating Error Variances of Three or More Atmospheric Data Sets – Part II: Evaluating Radio Occultation and Radiosonde Observations, Global Model Forecasts, and Reanalyses

2021 ◽  
Author(s):  
Therese Rieckh ◽  
Jeremiah P. Sjoberg ◽  
Richard A. Anthes
Keyword(s):  
Radio Occultation ◽  
State Of The Art ◽  
Specific Humidity ◽  
Data Sets ◽  
Error Growth ◽  
Atmospheric Conditions ◽  
Error Statistics ◽  
Weather And Climate ◽  
Atmospheric Data ◽  
The Impact

AbstractWe apply the three-cornered hat (3CH) method to estimate refractivity, bending angle, and specific humidity error variances for a number of data sets widely used in research and/or operations: radiosondes, radio occultation (COSMIC, COSMIC-2), NCEP global forecasts, and nine reanalyses. We use a large number and combinations of data sets to obtain insights into the impact of the error correlations among different data sets that affect 3CH estimates. Error correlations may be caused by actual correlations of errors, representativeness differences, or imperfect co-location of the data sets. We show that the 3CH method discriminates among the data sets and how error statistics of observations compare to state-of-the-art reanalyses and forecasts, as well as reanalyses that do not assimilate satellite data. We explore results for October and November 2006 and 2019 over different latitudinal regions and show error growth of the NCEP forecasts with time. Because of the importance of tropospheric water vapor to weather and climate, we compare error estimates of refractivity for dry and moist atmospheric conditions.

Unified, high resolution water quality retrievals from Earth Observation satellites

2021 ◽  
Author(s):  
James Harding
Keyword(s):  
Water Quality ◽  
Water Resource Management ◽  
State Of The Art ◽  
Earth Observation ◽  
Water Quality Parameter ◽  
Landsat 8 ◽  
Optical Parameters ◽  
Atmospheric Conditions ◽  
Mixture Density ◽  
Wide Range

<p>Earth Observation (EO) satellites are drawing considerable attention in areas of water resource management, given their potential to provide unprecedented information on the condition of aquatic ecosystems. Despite ocean colours long history; water quality parameter retrievals from shallow and inland waters remains a complex undertaking. Consistent, cross-mission retrievals of the primary optical parameters using state-of-the-art algorithms are limited by the added optical complexity of these waters. Less work has acknowledged their non- or weakly optical parameter counterparts. These can be more informative than their vivid counterparts, their potential covariance would be regionally specific. Here, we introduce a multi-input, multi-output Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in shallow and inland water bodies. The model is trained and validated using a sizeable historical database in excess of 1,000,000 samples across 38 optical and non-optical parameters, spanning 20 years across 500 surface waters in Scotland. The single network learns to predict concurrently Chlorophyll-a, Colour, Turbidity, pH, Calcium, Total Phosphorous, Total Organic Carbon, Temperature, Dissolved Oxygen and Suspended Solids from real Landsat 7, Landsat 8, and Sentinel 2 spectra. The MDN is found to fully preserve the covariances of the optical and non-optical parameters, while known one-to-many mappings within the non-optical parameters are retained. Initial performance evaluations suggest significant improvements in Chl-a retrievals from existing state-of-the-art algorithms. MDNs characteristically provide a means of quantifying the noise variance around a prediction for a given input, now pertaining to real data under a wide range of atmospheric conditions. We find this to be informative for example in detecting outlier pixels such as clouds, and may similarly be used to guide or inform future work in academic or industrial contexts. </p>

INTERFACES BETWEEN ATMOSPHERIC WATER HARVESTING AND SOLAR ENERGY: EVIDENCES FROM A BIBLIOMETRIC STUDY

2021 ◽  
Author(s):  
Ana Carolina Lamas da Silva ◽  
Elias Rocha Gonçalves Junior ◽  
Virgínia Siqueira Gonçalves
Keyword(s):  
Solar Energy ◽  
Bibliometric Study ◽  
Water Harvesting ◽  
Atmospheric Water

scholarly journals The residence time of water in the atmosphere revisited

2017 ◽  
Vol 21 (2) ◽  
pp. 779-790 ◽  
Author(s):  
Ruud J. van der Ent ◽  
Obbe A. Tuinenburg
Keyword(s):  
Residence Time ◽  
Hydrological Cycle ◽  
State Of The Art ◽  
Average Residence Time ◽  
Atmospheric Moisture ◽  
Atmospheric Water ◽  
Time And Space ◽  
Global Average ◽  
High Precipitation ◽  
Global Mean

Abstract. This paper revisits the knowledge on the residence time of water in the atmosphere. Based on state-of-the-art data of the hydrological cycle we derive a global average residence time of 8.9 ± 0.4 days (uncertainty given as 1 standard deviation). We use two different atmospheric moisture tracking models (WAM-2layers and 3D-T) to obtain atmospheric residence time characteristics in time and space. The tracking models estimate the global average residence time to be around 8.5 days based on ERA-Interim data. We conclude that the statement of a recent study that the global average residence time of water in the atmosphere is 4–5 days, is not correct. We derive spatial maps of residence time, attributed to evaporation and precipitation, and age of atmospheric water, showing that there are different ways of looking at temporal characteristics of atmospheric water. Longer evaporation residence times often indicate larger distances towards areas of high precipitation. From our analysis we find that the residence time over the ocean is about 2 days less than over land. It can be seen that in winter, the age of atmospheric moisture tends to be much lower than in summer. In the Northern Hemisphere, due to the contrast in ocean-to-land temperature and associated evaporation rates, the age of atmospheric moisture increases following atmospheric moisture flow inland in winter, and decreases in summer. Looking at the probability density functions of atmospheric residence time for precipitation and evaporation, we find long-tailed distributions with the median around 5 days. Overall, our research confirms the 8–10-day traditional estimate for the global mean residence time of atmospheric water, and our research contributes to a more complete view of the characteristics of the turnover of water in the atmosphere in time and space.

scholarly journals A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems

2022 ◽  
Vol 40 (2) ◽  
pp. 1-31
Author(s):  
Masoud Mansoury ◽  
Himan Abdollahpouri ◽  
Mykola Pechenizkiy ◽  
Bamshad Mobasher ◽  
Robin Burke
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
System Level ◽  
End User ◽  
Critical System ◽  
Long Tail ◽  
Fair Representation ◽  
Low Visibility ◽  
The Subject ◽  
Low Exposure

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness, where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end user but also for other stakeholders such as item sellers or producers who desire a fair representation of their items. This type of supplier fairness is sometimes accomplished by attempting to increase aggregate diversity to mitigate popularity bias and to improve the coverage of long-tail items in recommendations. In this article, we introduce FairMatch, a general graph-based algorithm that works as a post-processing approach after recommendation generation to improve exposure fairness for items and suppliers. The algorithm iteratively adds high-quality items that have low visibility or items from suppliers with low exposure to the users’ final recommendation lists. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, although it significantly improves exposure fairness and aggregate diversity, maintains an acceptable level of relevance of the recommendations.

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