Estimation Of Energy Balance Research Articles

91 Awardee Talk: Using respiration calorimetry to estimate energy balance to estimate energy balance and methane emissions

Abstract Understanding relationships between chemical components of the diet and methane production will allow nutritionists to develop formulation strategies to decrease methane emissions. Diets that produce acetate liberate hydrogen that can be used by methanogenic archaea to produce methane. Propionate is a hydrogen sink and diets that lead to increased propionate production decrease acetate and methane production. The objective was to evaluate relationships between methane emissions and dietary chemical components and feed intake using published databases and make diet formulation recommendations that could be used to decrease methane production in finishing beef cattle. A total of 263 treatment mean observations from respiration calorimetry studies with beef cattle and dairy steers and heifers were used to evaluate relationships between methane emissions, dietary chemical components, and feed intake. Among the chemical components evaluated, dietary neutral detergent fiber and starch were strongly related to methane yield. The relationship between dietary ether extract and methane yield was variable and there was no relationship between dietary crude protein concentration and methane yield. Additionally, the ratio of metabolizable energy relative to gross energy intake had a strong relationship to methane yield. Formulation strategies to increase dietary starch and decrease dietary neutral detergent fiber should allow the feedlot industry to decrease enteric methane production.

Estimation of Energy Balance throughout the Growing–Finishing Stage of Pigs in an Experimental Pig Barn

Monitoring the energy inputs and outputs in pig production systems is crucial for identifying potential imbalances and promoting energy efficiency. Therefore, the objective of this study was to measure the energy input, output, and losses during the growing–finishing phase of pigs from 1 September to 1 December 2023. A Livestock Environment Management System (LEMS) was used to measure the temperature, humidity, airflow, and water consumption levels inside the barn, and a load cell was used to measure the body weight of pigs. Furthermore, a bomb calorimetric test was conducted to measure the energy content of pigs’ manure. While calculating energy balance in the experimental barn, it was found that energy from feed and water contributed approximately 81% of the total input energy, while the remaining 19% of energy came from electrical energy. Regarding output energy, manure, and body weight accounted for about 69%, while around 31% was lost due to pig activities, maintaining barn temperature and airflow, and illuminating the barn. In conclusion, this study suggested methods to calculate energy balance in pig barns, offering valuable insights for pig farmers to enhance their understanding of input and output energy in pig production.

Actual evapotranspiration and energy balance estimation from vineyards using micro-meteorological data and machine learning modeling

Actual evapotranspiration (ETa) can be commonly estimated using numerical models based on i) weather and plant-based parameters, ii) from remotely sensed data and energy balance algorithms, and lately, iii) through the development and implementation of machine learning (ML) modeling techniques. In this work, supervised ML models were developed from a vineyard located in Talca, Chile, (i) to estimate actual evapotranspiration (ETa) (Model 1; M1) using the micrometeorological approach [Eddy Covariance; EC; sensible (H), latent (LE), soil heat fluxes (G) and net radiation (Rn)] and data from an automatic meteorological station (AMS) in reference conditions as ground-truth (inputs); (ii) to estimate energy balance components (Model 2; M2) from AMS data (inputs) and EC energy balance data as targets; (iii) to estimate ETa from the EC’s measured ETa data as target and thermal time data (degree hours; DH) calculated from air temperature with a base of 5 °C increments from 5 – 45 °C as inputs (Model 3; M3) and iv) to estimate energy balance components (targets from EC) from the same inputs of Model 3 (Model 4; M4). Results showed that the developed ML models had high accuracy and performance with no signs of over or under-fitting with a high correlation (R) and slope (b) close to unity (M1; R=0.94; b=0.89; M2; R=0.97; b=0.93; M3; R=0.97; b=0.89–0.95; M4; R=0.98; b=0.97). Furthermore, models were directly deployed over another vineyard located 22 km West of the modeled vineyard at 60 m lower over the sea level with significant performances and R values (R = 0.64–0.87; b = 0.66–1.00 for M1 to M4, respectively). These models could be used for precision irrigation to increase water use efficiency and better control canopy vigor, balance fruit and vegetative components, and ultimately improve berry and wine quality traits.

Improvement of Albedo and Snow-Cover Simulation during Snow Events over the Tibetan Plateau

Abstract The snow albedo is a vital component of land–atmosphere coupling models. It plays a critical role in regulating land surface energy exchange by controlling incoming solar radiation absorbed by the land surface and influencing the timing and rate of snowmelt. Accurate snow albedo simulation is essential to obtain surface energy balance and snow-cover estimates. Here, the simulation of albedo and snow cover using the Weather Research and Forecasting Model and an improved snow albedo scheme is verified against satellite-retrieved products during and immediately following eight snowfall events over the Tibetan Plateau. The improved model successfully characterizes the spatial pattern and inverted U-shaped temporal pattern of albedo over the entire Tibetan Plateau. This is attributed to the local optimization of snow-age parameters and explicit consideration of snow depth in the improved scheme. Compared with the previous model, the model proposed herein greatly decreases the overestimated albedo (by 0.13–0.27), yielding a bias range of ±0.08, mean relative bias decrease of 70%, and significant increase in the spatial correlation coefficient of 0.03–0.39 (mean: 0.13). The significant improvements of albedo estimates appear in deep snow-covered regions, largely attributed to parameter optimization related to snow albedo decay, while less improvements appear over the shallow snow-covered regions. Accurate reproduction of the spatiotemporal variation in albedo alleviated snow-cover overestimation by small amounts. For snow-cover estimates, the improved model consistently decreases the false-alarm rate by 0.03, and increases the overall accuracy and equitable threat score by 0.04 and 0.03, respectively. Moreover, the improved scheme shows an equivalent improvement of albedo estimates at both 1- and 5-km grid spacing over the eastern Tibetan Plateau; this is also true for snow-cover estimates. Significance Statement Snow albedo schemes in widely used numerical weather prediction models show notable shortcomings in complex mountainous regions, hindering accurate surface energy balance and snow-cover prediction. The purpose of this study is to better understand the role of snow albedo on snow-cover estimates and reveal the application potential of an improved snow albedo scheme across the Tibetan Plateau. This is important because snow albedo influences the timing and rate of snowmelt, and in turn snow-cover estimates, through regulating the surface energy budget. Our results highlight the strong application potential of our improved scheme in reducing snow simulation errors, confirm the importance of snow depth on snow albedo, and provide a new perspective for improving the accuracy of snow forecast over the topographically high Tibetan Plateau.

A Coupling Calculation Method of Desorption Energy Distribution Applied to CO2 Capture by Chemical Absorption

The pursuit of low-energy-consumption CO2 capture technology has promoted the renewal and iteration of absorbents for chemical absorption. In order to evaluate the regeneration energy consumption of absorbents and obtain the distribution of energy consumption, a coupling method combining rigorous energy balance and simple estimation is proposed in this study. The data regarding energy balance and material balance from process simulation are transformed into the model parameters required in the simple estimation model. Regenerative energy consumption and distribution are determined by the empirical estimation formula. Two CO2 capture processes of an MEA aqueous solution and MEA–n-propanol aqueous solution (phase-change absorbent) were used to verify the feasibility and applicability of the coupling method. The effects of n-propanol concentration, CO2 loading in the lean solution, and temperature on energy consumption were discussed. The results show that the energy consumption of 30 wt% MEA aqueous solution is the lowest at 3.92 GJ·t−1-CO2 when CO2 load in the lean solution is 0.2 mol CO2·mol−1-MEA, and the reaction heat Qrec, sensible heat Qsen, and latent heat Qlatent were 1.97 GJ·t−1-CO2, 1.09 GJ·t−1-CO2, and 0.86 GJ·t−1-CO2, respectively. The lowest energy consumption of the phase-change absorbent with CO2 loading of 0.35 mol CO2·mol−1-MEA in the lean solution is 2.32 GJ·t−1-CO2. Qrec, Qsen, and Qlatent were 1.9 GJ·t−1-CO2, 0.29 GJ·t−1-CO2, and 0.14 GJ·t−1-CO2, respectively. This study provides a simple and meaningful method for accurately assessing absorber performance and process improvement, which can accelerate the development of economically viable, absorption-based CO2 capture processes.

Assessing human internal exposure to chemicals at different physical activity levels: A physiologically based kinetic (PBK) model incorporating metabolic equivalent of task (MET)

Physical activity levels have the potential to impact human internal exposure to environmental chemicals. However, the current lack of simple modeling approaches hinders the high-throughput screening of chemical exposure at different physical activity levels. To address this gap, this study proposes a straightforward model for assessing human internal exposure to chemicals. Our approach is based on the physiologically based kinetic (PBK) model and utilizes the metabolic equivalent of task (MET) to characterize internal exposure to chemicals at varying activity levels. To facilitate the application of this model, we have developed an Excel-based operation tool, allowing users to easily modify the MET value and generate simulation results for different physical activity levels. The simulation results demonstrate that as physical activity levels increase, the biotransfer factors (BTFs) of chemicals decrease, suggesting that higher physical activity levels reduce the bioaccumulation potential of chemicals. The intensified physical activity enhances the overall elimination kinetics of chemicals from the human body. However, the simulated concentrations of chemicals in the human body increase with higher physical activity levels, due to the significantly increased external exposure to chemicals, such as through inhalation. Our proposed modeling approach, along with the operational tool, enables high-throughput simulation of human chronic internal exposure to chemicals at different physical activity levels, where the findings can assist in screening chemicals for further health risk assessment. To accomplish this, the model incorporates certain assumptions and utilizes generic model input values. However, due to the intricate nature of the interaction between external and internal exposures at different physical activity levels, validating the simulation through experimental studies becomes challenging and is not performed in this study. For future studies, we recommend incorporating more MET-related physiological input variables, improving energy balance estimates, comprehending external exposure estimates, and conducting cohort studies to enhance and validate the proposed modeling approach.

Energy balance determination of crop evapotranspiration using a wireless sensor network

Determining crop evapotranspiration (ET) is essential for managing water at various scales, from regional water accounting to farm irrigation. Quantification of ET may be carried out by several procedures, being eddy covariance and energy balance the most established methods among the research community. One major limitation is the high cost of the sensors included in the eddy covariance or energy balance systems. We report here the development of a simpler device (CORDOVA-ET: COnductance Recording Device for Observation and VAlidation of ET) to determine crop ET based on industrial-grade, commercial off-the-shelf (COTS) sensors costing far less than research-grade sensors. The CORDOVA-ET contains a sensor package that integrates the basic micrometeorological instrumentation and the infrared temperature sensors required for estimating ET over crops using the energy balance approach. One novel feature is the presence of four different nodes that allow the determination of ET in four different locations within a field or in four different fields of the same crop, thus allowing an assessment of ET spatial variability. The system was conceived as an open-source and hardware alternative to commercial devices, using a collaborative approach for the development of a regional ET network in countries of North Africa and the Near East. Comparisons of radiation, temperature, humidity, and wind against those of research-grade sensors yielded excellent results, with coefficients of correlation (R2) above 0.96. The estimated reference ET calculated from these measurements showed R2 = 0.99 and a root mean square error (RMSE) of 0.22 mm/day. The infrared temperature measurements at the four different nodes showed an RMSE below 0.56°C. The energy balance components and estimates of ET from the CORDOVA-ET were validated against an eddy-covariance system over a wheat crop. The high (R2) for net radiation (0.98), sensible heat (0.88), and latent heat (0.86) showed good agreement between the modeled energy fluxes and the field measurements. The hardware components, acquisition, and data processing software are available as open-source repositories to facilitate adoption for different applications, from water use efficiency research to irrigation management.

Techno-economic feasibility of fluid catalytic cracking unit integrated chemical looping combustion – A novel approach for CO2 capture

Oil refineries are collectively responsible for about 4–6% of the global CO2 emissions, largely because of the regenerator part of the Fluid Catalytic Cracking (FCC) unit (25–35%). An advanced combustion technology, also called chemical looping combustion (CLC), has been recently presented as a novel CO2 capture process for FCC units; however, no study provides the economic feasibility of a CLC-FCC unit. In this study, a techno-economic feasibility of the novel CLC-FCC unit was presented for the first time based on a case study with 50,000 barrels feed per day. A rigorous mass and energy balance estimation shows that 96 vol% of coke regeneration (combustion) was achieved in the FCC regenerator by using a stoichiometrically required amount of metal oxide (CuO modified catalysts) at 750 °C for 45 min. The preliminary energy penalty calculations of the proposed CLC-FCC unit (0.21 GJ/ton CO2) is relatively lower compared to the post-combustion (3.1–4.2 GJ/t CO2) via amine solvent and oxy-fuel combustion (1.8–2.5 GJ/t CO2) units reported in the literature. The equipment purchase cost (EPC) is 1.1 times higher than a standalone FCC unit due to the increase in the number of processing equipment required. The cash flow analysis results reveal a yearly basis average CO2 capture cost of 0.0106 US$/kg of CO2 (∼10.6 US$/ton CO2) for the CLC-FCC unit, which is lower compared to the other conventional CCS technologies i.e. oxy-fuel combustion and post-combustion. Factors such as EPC, capital expenditure (CAPEX), and discount rate significantly influenced the capture cost. In contrast, the CO2 capture cost is not influenced by a change in oxygen carrier and electricity cost.

A novel semi-empirical model for crop leaf area index retrieval using SAR co- and cross-polarizations

The retrieval of continuous leaf area index (LAI) in space and time from remote sensing is beneficial for cropland monitoring and management. Synthetic Aperture Radar (SAR) with the advantages of all-weather operation and fine spatial resolutions has been utilized in various agricultural applications. Although the water cloud model (WCM) has been extensively used for LAI estimation over croplands, it is modified for application to crop canopies with large gaps. The requirement of prior information on soil moisture is also a hinderance for the model application. In this study, WCM is theoretically modified to consider the correlation of active microwave propagation through the canopy in downward and upward directions. Such a modification is particularly important for sparse vegetation with large gaps between crop plants in the early stage of the growing season. A parameter with explicit physical meaning, i.e. the full-vegetation backscattering coefficient, was defined to simplify our model scheme and make the model applicable for the whole growing season. In addition, a physics-based SAR data processing scheme is developed to entangle the influences of LAI and soil moisture on SAR backscatter by taking advantage of the multiple polarizations of RADARSAT-2 (R2) SAR data. In this way, LAI was estimated using modified WCM without the prior knowledge of soil moisture. To evaluate LAI retrieved from the R2 datasets (R2 LAI), ground-based LAI measurements were made at the experimental area of SMAPVEX16-MB in Canada with twenty approximately 800 m х 800 m plots in soybeans and corn. R2 LAI was well correlated to these ground-based LAI (n = 15, R2 = 0.63, RMSE = 0.99 m2·m−2 for soybeans; n = 5, R2 = 0.66, RMSE = 1.20 m2·m−2 for corn). LAI was also retrieved from optical data acquired by Sentinel-2/MSI (S2), denoted as S2 LAI. The R2 LAI and S2 LAI are well correlated and achieved coefficients of determination (R2) of 0.76 and 0.71 and root mean square errors (RMSE) of 1.1 and 1.4 m2·m−2 for soybeans and corn, respectively. The seasonal variations of R2 LAI and S2 LAI are generally similar except at the end of the growing season when S2 LAI is considerably larger than R2 LAI. S2 LAI followed the trend of the reduction in leaf chlorophyll content, while R2 LAI reduced only slightly due to the decrease in leaf water content near the end the growing season. R2 LAI represents the total standing leaf area useful for surface energy balance estimation, while S2 LAI responds to green leaf area useful for crop productivity modeling.

Assessing the Performance of the South American Land Data Assimilation System Version 2 (SALDAS-2) Energy Balance across Diverse Biomes

Understanding the exchange of energy between the surface and the atmosphere is important in view of the climate scenario. However, it becomes a challenging task due to a sparse network of observations. This study aims to improve the energy balance estimates for the Amazon, Cerrado, and Pampa biomes located in South America using the radiation and precipitation forcing obtained from the Clouds and the Earth’s Radiant Energy System (CERES) and the precipitation CPTEC/MERGE datasets. We employed three surface models—Noah-MP, Community Land Model (CLSM), and Integrated Biosphere Simulator (IBIS)—and conducted modeling experiments, termed South America Land Data Assimilation System (SALDAS-2). The results showed that SALDAS-2 radiation estimates had the smallest errors. Moreover, SALDAS-2 precipitation estimates were better than the Global Land Data Assimilation System (GLDAS) in the Cerrado (MBE = −0.16) and Pampa (MBE = −0.19). Noah-MP presented improvements compared with CLSM and IBIS in 100% of towers located in the Amazon. CLSM tends to overestimate the latent heat flux and underestimate the sensible heat flux in the Amazon. Noah-MP and Ensemble outperformed GLDAS in terms latent and sensible heat fluxes. The potential of SALDAS-2 should be emphasized to provide more accurate estimates of surface energy balance.

Effects of accelerometry-derived physical activity energy expenditure on urinary C-peptide levels in a wild primate (Papio ursinus)

Animals have finite energy reserves for growth, survival, and reproduction and must maintain a stable energy balance. Measuring energy balance in the wild, however, is beset with methodological challenges. Quantification of urinary C-peptide (uCP), a proxy for insulin secretion, has enabled researchers to non-invasively estimate energy balance, and positive relationships between uCP levels and energy intake have been documented in numerous non-human primates. Comparatively few studies show that, consistent with insulin physiology, energy expenditure also alters levels of uCP. The timescale and extent of this relationship, however, remains unclear given the reliance on crude measures of activity and inferred energy expenditure. Here, for the first time, we test for effects of accelerometer-derived Vectorial Dynamic Body Acceleration (VeDBA) - a continuous measure of physical activity energy expenditure - on urinary C-peptide (uCP) levels in n = 12 wild chacma baboons (Papio ursinus). Applying a model selection approach, we show that VeDBA summed over short timescales (30 min to 1 h) prior to urine collection was negatively associated with uCP levels. Using the acceleration-based time individuals spent ‘non-stationary’ (i.e. locomoting) prior to urine collection as a predictor - instead of summed VeDBA - revealed similar but less clear results. Overall, the negative relationship between VeDBA and uCP levels highlights the importance of quantifying physical activity energy expenditure when using uCP measures to estimate energy balance and has potential implications for the field of energetics accelerometry.

Quantitative Evaluation of Runoff Simulation and Its Driving Forces Based on Hydrological Model and Multisource Precipitation Fusion

The hydrological cycle across the source regions of the Yellow River (SRYR) affects water supply for 324 million people across the Yellow River basin (YRB), and the scarcity of meteorological stations leads to great challenges for the estimation of hydrologic and energy balance. Therefore, our work employs multisource precipitation products across the YRB to develop a new integrated precipitation product with the optimized Bayesian mean algorithm (OBMA). It investigates the performance and hydrological utility of the optimal Bayesian integrated precipitation product (OBIPP). This study found that the OBIPP improved by 14.08% in overall performance relative to the optimal precipitation product across the SRYR, respectively. Meanwhile, the variable infiltration capacity (VIC) model, driven by daily OBIPP, can drastically improve the accuracy of runoff simulation compared with other precipitation products across the SRYR. According to the VIC model driven by daily OBIPP, the average precipitation and runoff depth across the SRYR were approximately 621 mm and 64 mm from 2001 to 2019, respectively, showing a spatial trend increasing from northwest to southeast. Overall, OBIPP is characterized by smaller uncertainty of simulation and higher simulation performance across the SRYR, which should provide a scientific basis for accurate prediction and assessment of water resources in areas where meteorological data are scarce.

Photovoltaic energy balance estimation based on the building integration level

The photovoltaic module building integration level affects the module temperature and, consequently, its output power. In this work, a methodology has been proposed to estimate the influence of the level of architectural photovoltaic integration on the photovoltaic energy balance with natural ventilation or with forced cooling systems. The developed methodology is applied for five photovoltaic module technologies (m-Si, p-Si, a-Si, CdTe, and CIGS) on four characteristic locations (Athens, Davos, Stockholm, and Würzburg). To this end, a photovoltaic module thermal radiation parameter, PVj, is introduced in the characterization of the PV module technology, rendering the correlations suitable for building-integrated photovoltaic (BIPV) applications, with natural ventilation or with forced cooling systems. The results show that PVj has a significant influence on the energy balances, according to the architectural photovoltaic integration and climatic conditions.

Research on Cracked Conditions in Nickel Chrome Alloy Ni50Cr33W4.5Mo2.8TiAlNb, Obtained by Direct Laser Deposition

Nowadays, additive manufacturing (AM) is a powerful way to make complex-shaped components for airspace engineering from nickel-based superalloys. So, while nickel-based superalloys could easily be we L-DED in sheet-metal thicknesses, they suffered from strain-age cracking and solidification during AM or in the post-weld aging treatment. This is attributed to the fact that besides the limitation of γ′- phase forming elements (Al and Ti), as they form by AM very rapidly and reduce ductility, the majority of the superalloys contain carbide-forming elements such as Cr, Mo, and W. The precipitation of carbides, which is very effective in strengthening, develops cracks in the heat-affected zone (HAZ) during AM. The difference in isochoric heat capacities and the thermal expansion coefficient (TEC) at the phase boundary leads to the appearance of dangerous local destruction energy. If the area of the interfacial interface is sufficiently extended, then the accumulation of this energy reaches a level sufficient for a crack formation. We have proposed a crack initiation criterion (CIC) for assessing the dangerous level of fracture energy. The CIC was derived from an estimate of the local energy balance from the heat transfer equation for the two-phase area. Practical approbation of the criterion was carried out after L-DED of samples from Ni50Cr33W4.5Mo2.8TiAlNb (EP648) alloy powder with an increased carbon content based on the study of the chemical composition near the crack formed during solidification. Using the proposed criterion provides an opportunity to give the rank to carbide-forming elements according to the degree of their influence on the fracture energy. Thus, the release of aluminum carbide turned out to be 5.48 times more dangerous than the release of titanium carbide and more than 5 times more dangerous than the release of tungsten carbide and molybdenum.

Enhanced methane production using pretreated sludge in MEC-AD system: Performance, microbial activity, and implications at different applied voltages

Although various pretreatment methods are employed to promote sludge hydrolysis and thereby promoting methane production in the subsequent microbial electrolysis cell assisted anaerobic digestion (MEC-AD) system, the questions arise are, “which pretreatment method on waste activated sludge (WAS) maximises the sludge hydrolysis and what is the optimal applied voltage on anaerobic digestion (AD) to stimulates the direct interspecies electron transfer (DIET) performance and thereby accelerating the methane production fed with pretreated WAS?” was still unanswered. Herein, firstly, a series of pretreatment methods to hydrolyse and mineralise the organic matter of WAS was performed to evaluate solubilization efficiency and thereafter, the influence of different applied voltages (0.3 V, 0.6 V, and 0.9 V) on coupled MEC-AD reactors fed with pretreated WAS was investigated to apprehend the DIET promotion for methane production. The results indicated that in MEC-AD reactors, the methane yield increased by 27.2%, 44.8%, and 37.3% when the applied voltages were 0.3 V, 0.6 V, and 0.9 V, respectively. Therefore, the alkaline-thermal pretreatment (ATP) enhanced the sludge hydrolysis in WAS, followed by an applied voltage of 0.6 V in the MEC-AD reactor fed with pretreated WAS, enhanced methane production under DIET stimulation induced by the increased abundance of electroactive microorganisms (EAM) and the advanced electron transfer. Besides, the energy balance estimation validates that with an applied voltage of 0.6 V in MEC-AD could achieve higher net energy input.

Protocol of Energy balance estimates in Huntington's disease: an observational, case-control, multicenter pilot study. (Preprint)

Protocol of Energy balance estimates in Huntington's disease: an observational, case-control, multicenter pilot study. (Preprint)

A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan

Abstract. From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. While developed for a specific project, these data are publicly available and useful for other applications that require hydrologic estimates of the water and energy balance. These two data streams are unique because of their suitability for routine monitoring, as well as for being a historical record for computing relative indicators of water availability. The global stream is available at ∼ 1-month latency, and monthly average outputs are on a 10 km grid from 1982–present. The second data stream, Central Asia (21–56∘ N, 30–100∘ E), at ∼ 1 d latency, provides daily average outputs on a 1 km grid from 2000–present. This paper describes the configuration of the two FLDAS data streams, background on the software modeling framework, selected meteorological inputs and parameters, and results from previous evaluation studies. We also provide additional analysis of precipitation and snow cover over Afghanistan. We conclude with an example of how these data are used in integrated food security analysis. For use in new and innovative studies that will improve understanding of this region, these data are hosted by U.S. Geological Survey data portals and the National Aeronautics and Space Administration (NASA). The Central Asia data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/VQ4CD3Y9YC0R (Jacob and Slinski, 2021), and the global data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/5NHC22T9375G (McNally, 2018).

Personalized Self-Monitoring of Energy Balance through Integration in a Web-Application of Dietary, Anthropometric, and Physical Activity Data

Self-monitoring of weight, diet and physical activity is a valuable component of behavioral weight loss treatment. The validation and user-friendliness of this approach is not optimal since users are selected from homogeneous pools and rely on different applications, increasing the burden and achieving partial, generic and/or unrelated information about their metabolic state. Moreover, studies establishing type, time, duration, and adherence criteria for self-monitoring are lacking. In this study, we developed a digital web-based application (ArmOnIA), which integrates dietary, anthropometric, and physical activity data and provides a personalized estimation of energy balance. Moreover, we determined type, time, duration, and adherence criteria for self-monitoring to achieve significant weight loss in a highly heterogeneous group. A single-arm, uncontrolled prospective study on self-monitored voluntary adults for 7 months was performed. Hierarchical clustering of adherence parameters yielded three behavioral approaches: high (HA), low (LA), and medium (MA) adherence. Average BMI decrease is statistically significant between LA and HA. Moreover, we defined thresholds for the minimum frequencies and duration of dietary and weight self-monitoring. This approach can provide the correct clues to empower citizens with scientific knowledge, augmenting their self-awareness with the aim of achieving long-lasting results when pursuing a healthy lifestyle.

Observed and Modeled Urban Heat Island and Sea-Breeze Circulation Interactions: A Shanghai Case Study

Abstract Urban heat island (UHI) and sea–land-breeze systems are well-known and important characteristics of the climate of coastal cities. To model these, the accurate estimation of the surface energy balance (SEB) is a key factor needed to improve local-scale simulations of thermodynamic and dynamic boundary circulations. The Weather Research and Forecasting Model with a single-layer urban canopy model (WRF/SLUCM), with parameters derived from MODIS and local GIS information, is used to investigate the UHI and sea-breeze circulations (SBC) in the megacity of Shanghai. The WRF/SLUCM can reproduce observed urban radiation and SEB fluxes, near-surface meteorological variables, and the evolution of the UHI and SBC. Simulations for an August period show the maximum UHI tends to drift northwest in the afternoon, driven by the prevailing southeast wind. The sea breeze lasts for about 4 h and is strongest between 1200 and 1400 local time (UTC + 8 h). The interaction between UHI and SBC is evident with low-level convergence, upward motion, and moisture transport from the sea and urban breezes simulated. An urban circulation (horizontal/vertical/time scales: ∼20 km/∼1.5 km/∼3 h) with thermal vertical motions (∼1.5 m s−1) above the urban area and an SBC (horizontal/vertical/time scales: 6–7 km/∼1 km/2–3-h) above the northern coastal suburb occur. Combined the sea breeze and southerly winds form a low-level wind shear (convergence zone) ∼5 km from the coast that penetrates ∼20 km inland to the urban center. Using the WRF/SLUCM simulations we improve understanding of the complex spatial dynamics of summertime urban heating in coastal megacities, such as Shanghai.

Progress in Developing Scale-Able Approaches to Field-Scale Water Accounting Based on Remote Sensing

To increase water productivity and assess water footprints in irrigated systems, there is a need to develop cheap and readily available estimates of components of water balance at fine spatial scales. Recent developments in satellite remote sensing platforms and modelling capacities have opened opportunities to address this need, such as those being developed in the WaterSENSE project. This paper showed how evapotranspiration, soil moisture, and farm-dam water volumes can be quantified based on the Copernicus data from the Sentinel satellite constellation. This highlights distinct differences between energy balance and crop factor approaches and estimates that can be derived from the point scale to the landscape scale. Differences in the results are related to assumptions in deriving evapotranspiration from remote sensing data. Advances in different parts of the water cycle and opportunities for crop detection and yield forecasting mean that crop water productivity can be quantified at field to landscape scales, but uncertainties are highly dependent on input data availability and reference validation data.