Large Uncertainties Research Articles

Earth Observations, Digital Footprints and Machine-Learning: Greenhouse Gas Stocktaking for Climate Change Mitigation

Introduction & BackgroundMethane (CH4) is a powerful greenhouse gas, leaving both a physical and digital footprint from natural (40%) and human (60%) sources. Its atmospheric concentration has increased from 722 ppb before the industrial age to ~1,922 ppb in recent times. Because of its global warming potential, measuring and monitoring CH4 is crucial to mitigating the impacts of climate change. However, large uncertainties exist in “bottom-up” inventories (a product of activity data based on counts of components, equipment or throughput, and estimates of gas-loss rates per unit of activity for different land uses) reported to the United Nations Framework Convention on Climate Change, making it difficult for policymakers to set emission reduction targets. To address this, we employ causality-constrained machine learning (ML) to combine different gas observations from satellite sensors onboard the TROPOspheric Monitoring Instrument (which measure a digital footprint of human methane-generating behaviour) with outputs from chemical modelling. These are linked with datasets from the national statistics office, meteorology office and a comprehensive survey on quality of life in the emission field, to improve bottom-up estimates of CH4 emissions at the Earth’s surface. Objectives & ApproachThe research uses mixed methods for collecting and analysing both qualitative and quantitative data for multidisciplinary processing strategies for monitoring CH4 emissions locally and regionally. It also assesses whether additional “digital footprint” variables besides the well-known chemical sources and sinks can be studied to improve our understanding of the CH4 budget. We have conducted an “analytical inversion” of satellite observations of CH4 to obtain emission fluxes. These represent the dependent variable for our ML model, in combination with 22 independent variables (co-occurring trace gases, meteorological fields, land use, land cover, population, livestock, and data from a survey of quality of life from the Gauteng City-Region Observatory, covering a broad range of socio-economic, personal and political issues) with near-real-time Earth observation data, to aid the development of a causality-constrained ML model for the prediction of CH4 fluxes. Relevance to Digital FootprintsWe make use of not only satellite imagery, but socio-economic, demographic, and environmental data, and repurpose it for environmental sustainability in the context of mitigating climate change. We are creating unique resources in documenting rapid changes in emissions. Conclusions & ImplicationsThis research will make important contributions to developing countries with limited resources, enabling them to contribute to the global stocktake towards net-zero by helping policymakers identify geographic regions that are major emitters, enabling them to put measures into place to mitigate emissions.

The DECOVALEX international collaboration on modeling of coupled subsurface processes and its contribution to confidence building in radioactive waste disposal

The long-lived radiotoxicity of the high-level radioactive waste generated by nuclear power plants requires safe isolation from the biosphere for many hundreds of thousands of years. An international consensus has emerged that such isolation can best be provided by disposal in mined geologic repositories, a strategy that today is pursued by most countries dealing with radioactive waste. However, the need to predict the performance of such repositories over very long time periods generates large uncertainties that have to be accounted for in safety assessments. The findings from such safety assessments need to be conveyed to all stakeholders in a clear way, such that public confidence in geologic disposal solutions can be achieved. It is suggested here that close international collaboration on the technical aspects of geologic waste disposal has helped, and will continue to help, building trust and increasing confidence. This paper discusses a particular international collaboration initiative referred to as DECOVALEX, which brings together multiple teams and disciplines to collectively tackle complex experimental and modeling challenges related to geologic disposal. By describing how DECOVALEX works and by providing joint research examples, a case is made that such international collaboration contributes to knowledge transfer and confidence building in radioactive waste disposal science.

Satellite-derived sandy shoreline trends and interannual variability along the Atlantic coast of Europe

Monitoring sandy shoreline evolution from years to decades is critical to understand the past and predict the future of our coasts. Optical satellite imagery can now infer such datasets globally, but sometimes with large uncertainties, poor spatial resolution, and thus debatable outcomes. Here we validate and analyse satellite-derived-shoreline positions (1984–2021) along the Atlantic coast of Europe using a moving-averaged approach based on coastline characteristics, indicating conservative uncertainties of long-term trends around 0.4 m/year and a potential bias towards accretion. We show that west-facing open coasts are more prone to long-term erosion, whereas relatively closed coasts favor accretion, although most of computed trends fall within the range of uncertainty. Interannual shoreline variability is influenced by regionally dominant atmospheric climate indices. Quasi-straight open coastlines typically show the strongest and more alongshore-uniform links, while embayed coastlines, especially those not exposed to the dominant wave climate, show weaker and more variable correlation with the indices. Our results provide a spatial continuum between previous local-scale studies, while emphasizing the necessity to further reduce satellite-derived shoreline trend uncertainties. They also call for applications based on a relevant averaging approach and the inclusion of coastal setting parameters to unravel the forcing-response spectrum of sandy shorelines globally.

Peak pressures on high-rise buildings roof: A dual approach through validated LES and wind tunnel experiments with uncertainty quantification

Understanding the accuracy of predicted peak pressure in wind engineering is crucial for effective risk assessment and structural design. This study investigates the capacity of large eddy simulation (LES) in predicting peak pressure coefficients on the flat roof and facade of a high-rise building, incorporating uncertainty quantification from 11-hour equivalent full-scale experimental measurements. The isolated and group configurations under 0° and 45° wind angles are analysed.The performance of two estimation methods for peak pressure coefficients – the traditional “epochal” approach and peaks over threshold (POT) – is evaluated, determining the optimal set of parameters that minimises uncertainty. While the stationarity test reaches satisfactory residuals for a 75-minute equivalent full-scale duration, 25- and 37.5-minute durations are also analysed. The traditional approach is preferred for its reliability and consistency, although POT notably excels in capturing higher peak pressure coefficients.Numerical results demonstrate negligible differences across all analysed durations and align well with experimental findings. A 25-minute equivalent full-scale duration is found to be a suitable representative of the entire signal. However, notable discrepancies near the upwind corners on the roof, with larger uncertainty in experimental data, underscore challenges in prediction accuracy for these regions. It emphasises importance of individual treatment and validation for each case.

Two-dimensional Eclipse Mapping of the Hot-Jupiter WASP-43b with JWST MIRI/LRS

We present eclipse maps of the two-dimensional thermal emission from the dayside of the hot-Jupiter WASP-43b, derived from an observation of a phase curve with the JWST MIRI/LRS instrument. The observed eclipse shapes deviate significantly from those expected for a planet emitting uniformly over its surface. We fit a map to this deviation, constructed from spherical harmonics up to order ℓmax=2 , alongside the planetary, orbital, stellar, and systematic parameters. This yields a map with a meridionally averaged eastward hot-spot shift of (7.75 ± 0.36)°, with no significant degeneracy between the map and the additional parameters. We show the latitudinal and longitudinal contributions of the dayside emission structure to the eclipse shape, finding a latitudinal signal of ∼200 ppm and a longitudinal signal of ∼250 ppm. To investigate the sensitivity of the map to the method, we fix the parameters not used for mapping and derive an “eigenmap” fitted with an optimized number of orthogonal phase curves, which yields a similar map to the ℓmax=2 map. We also fit a map up to ℓmax=3 , which shows a smaller hot-spot shift, with a larger uncertainty. These maps are similar to those produced by atmospheric simulations. We conclude that there is a significant mapping signal which constrains the spherical harmonic components of our model up to ℓmax=2 . Alternative mapping models may derive different structures with smaller-scale features; we suggest that further observations of WASP-43b and other planets will drive the development of more robust methods and more accurate maps.

The pitfall in designing future electrical power systems without considering energy return on investment in planning

Measuring the limitations of transitioning to 100% renewable energy systems partly equates with the capability of quantifying associated net-energy production. To foresee future possibilities, an advanced systemwide energy-return-on-investment (EROI) model was constructed. Driven by the thorough consideration of the existing weaknesses of EROI, the model integrates the concept of energy statistics, life cycle assessment techniques and energy system modelling in order to overcome the underlying primary energy quality issues, boundary condition problems and energy system design related uncertainties that compromises comparability of EROI during the energy transition. This model was applied to the Israeli power system that presents a unique opportunity to examine the vulnerability of depending on limited resources and the connection of EROI to system design. This study demonstrates the dependency of EROI on the energy transition path, system design requirements expressed via interactive linkages of curtailment, variable renewable energy penetration and storage system design, and resource diversity. Achieving a very high variable renewable energy penetration by 2050 with solar photovoltaics carries a risk of falling below an EROI value of 10, such vulnerability can be largely minimised by well-conducted manoeuvring of the system design and resource diversification. Despite the large uncertainty of this study, the need for a multicriteria designing technique is out of the question.

Intensification and Poleward Shift of Compound Wind and Precipitation Extremes in a Warmer Climate

AbstractCompound wind and precipitation extremes (CWPEs) can severely impact natural and socioeconomic systems. However, our understanding of CWPE future changes, drivers, and uncertainties under a warmer climate is limited. Here, by analyzing the event both on oceans and landmasses via state‐of‐the‐art climate model simulations, we reveal a poleward shift of CWPE occurrences by the late 21st century, with notable increases at latitudes exceeding 50° in both hemispheres and decreases in the subtropics around 25°. CWPE intensification occurs across approximately 90% of global landmasses, and is most pronounced under a high‐emission scenario. Most changes in CWPE frequency and intensity (about 70% and 80%, respectively) stem from changes in precipitation extremes. We further identify large uncertainties in CWPE changes, which can be understood at the regional level by considering climate model differences in trends of CWPE drivers. These results provide insights into understanding CWPE changes under a warmer climate, aiding robust regional adaptation strategy development.

A Spatially Resolved [C ii] Survey of 31 z ∼ 7 Massive Galaxies Hosting Luminous Quasars

The [C ii] 158 μm emission line and the underlying far-infrared (FIR) dust continuum are important tracers for studying star formation and kinematic properties of early galaxies. We present a survey of the [C ii] emission lines and FIR continua of 31 luminous quasars at z > 6.5 using the Atacama Large Millimeter Array (ALMA) and the NOrthern Extended Millimeter Array at sub-arcsec resolution. This survey more than doubles the number of quasars with [C ii] and FIR observations at these redshifts and enables statistical studies of quasar host galaxies deep into the epoch of reionization. We detect [C ii] emission in 27 quasar hosts with a luminosity range of L [C II] = (0.3–5.5) × 109 L ⊙ and detect the FIR continuum of 28 quasar hosts with a luminosity range of L FIR = (0.5–13.0) × 1012 L ⊙. Both L [C II] and L FIR are correlated (ρ ≃ 0.4) with the quasar bolometric luminosity, albeit with substantial scatter. The quasar hosts detected by ALMA are clearly resolved with a median diameter of ∼5 kpc. About 40% of the quasar host galaxies show a velocity gradient in [C ii] emission, while the rest show either dispersion-dominated or disturbed kinematics. Basic estimates of the dynamical masses of the rotation-dominated host galaxies yield M dyn = (0.1–7.5) × 1011 M ⊙. Considering our findings alongside those of literature studies, we found that the ratio between M BH and M dyn is about 10 times higher than that of local M BH–M dyn relation on average but with substantial scatter (the ratio difference ranging from ∼0.6 to 60) and large uncertainties.

A Flexible, Multi-Instrument Optimal Estimation Retrieval for Wind Profiles

Abstract Instruments such as Doppler lidars, radar wind profilers, and uncrewed aircraft systems could be used in observation networks to fill in the temporal and spatial gap that exists for low-level wind observations. These instruments, however, do not directly observe the wind and require a retrieval to be used to obtain wind estimates from their observations. Also, the depth and uncertainty of observations collected by these instruments vary depending on the environment that they are sampling. Optimal estimation is a variational retrieval method that combines information from a prior dataset and observations to retrieve an atmospheric state. This technique can be beneficial to use when observations have large uncertainties or provide insufficient information to obtain the atmospheric state by themselves. A new optimal estimation retrieval for obtaining wind profiles from typical lower atmospheric wind profiling instrumentation has been developed. This retrieval allows for more observations from wind profiling instrumentation to be used when retrieving wind profiles, increases the depth of retrieved profiles, and eliminates vertical data gaps. This retrieval can also be used to easily combine observations from different instruments or even with model data to create combined data wind retrievals that leverage the strengths of the different data sources to retrieve a wind profile that is superior to those obtained by the individual observations or data sources. It is envisioned that this retrieval will be continued to be developed and maintained as community software as lower atmospheric wind observing capabilities further develop and expand.

Underground Hydrogen Storage in Porous Media: The Potential Role of Petrophysics

The objective of this study is to showcase the key geological and reservoir engineering parameters that influence underground hydrogen storage, demonstrate the value of some petrophysical data, and show how hydrogen storage differs between depleted gas fields and saline aquifers for reservoir and geomechanical modeling. We utilized numerical simulation modeling to create a base-case model of a synthetic reservoir that accurately represented the hydrodynamic conditions relevant to underground hydrogen storage in porous media. A two-step sensitivity analysis was then conducted. Firstly, we identified the critical parameters that significantly influence the storage and flow of hydrogen in porous media. Subsequently, we analyzed the geomechanical impact of underground hydrogen storage. In addition, we compared the behavior of hydrogen storage to natural gas storage. The study showed that the reservoir depth or current pressure, the reservoir dip, and the flow capacity were the top three factors impacting the optimal withdrawal of hydrogen. The study also revealed that rock displacement and stress changes were important to be monitored, while changes in strain were not significant. If it is assumed that injection occurs in a critically stressed rock, hydrogen injection and withdrawal in saline aquifers could result in more incidence of microseismicity compared to hydrogen storage in depleted fields or even gas storage in depleted fields. This study quantifies uncertainties in data and pinpoints areas where petrophysical measurements could minimize the uncertainty associated with critical parameters relevant to underground hydrogen storage. It also identifies gaps in measurements for hydrogen storage in porous media. These parameters with large uncertainty are crucial for selecting optimal sites for hydrogen storage and detecting subsurface integrity issues when monitoring for underground hydrogen storage in porous media.

Model Diagnostic Analysis in a Cold Basin Influenced by Frozen Soils With the Aid of Stable Isotope

AbstractUnderstanding the hydrological processes on the Tibetan Plateau (TP) under climate change is an important scientific question. The frequent multiphase transfer exacerbates the complexity of hydrological processes on the TP, which brings equifinality problem to hydrological models and causes large uncertainties in quantifying the contributions of runoff components. Tracer‐aided hydrological models are helpful for improving model performances and have been adopted in cryospheric regions, but the influence of frozen soil has yet to be considered. This study adopted the Tracer‐aided Tsinghua Representative Elementary Watershed model (THREW‐T) in a typical cold basin with widespread frozen soil on the TP. The model structure was diagnosed with isotope by identifying the influences of frozen soil. A simplified catchment‐scale frozen soil module was incorporated into the model. Results showed that: (a) The THREW‐T model cannot simultaneously simulate baseflow and stream water isotope well. The imbalance of simulations on two objectives could be attributed to the influence of frozen soil, resulting in seasonal variation of soil‐related parameters, which was not considered in the model. (b) Incorporating the frozen soil module significantly improved the balance of baseflow and isotope simulation, simultaneously producing low baseflow and high contribution of subsurface runoff during wet seasons. (c) The frozen soil had little influence on the annual streamflow, but changed the runoff seasonality by reducing baseflow during dry seasons and increasing subsurface runoff during wet seasons. The frozen soil module was still simplified, and further work is needed to improve the physical representation of soil freeze‐thaw process. This study highlights the value of tracer‐aided hydrological modeling method on diagnosing model structure by identifying the influence of specific processes such as frozen soil.

Assessing mangrove cover change in Madagascar (1972–2019): Widespread mangrove deforestation is slowing down

Between the 1970s and early 2000s, Madagascar witnessed significant mangrove extent and condition declines due to deforestation and degradation. Data on mangrove extent prior to the early 1990s is scarce, contributing to uncertainties in long-term trend analyses. Recent estimates (since 2010) have reported widely varying mangrove extents from 2000 km² to 3400 km², indicating large uncertainties in current trends. Leveraging five decades of Landsat satellite data, this study offers a methodologically consistent approach to classify Madagascar's mangrove extent, providing a comprehensive dataset from 1972, 1989, 1999, 2009, to 2019. Our findings reveal an overall decrease of 8 % from 2935 km² in 1972–2699 km² in 2019, with loss rates decelerating from 0.4 % per year in the initial period to 0.2 % per year post-1999. This decline primarily reflects anthropogenic pressures, notably in the northern regions. Conversely, the last decade has witnessed a 5 % increase in mangrove coverage, primarily in the central and southern regions, thanks to concerted conservation and reforestation efforts, highlighting a positive shift in mangrove management and protection strategies. This study's long-term perspective is crucial for understanding the dynamics of mangrove coverage and guiding effective intervention strategies in Madagascar.

Collapsing domain wall networks: impact on pulsar timing arrays and primordial black holes

Unstable domain wall (DW) networks in the early universe are cosmologically viable and can emit a large amount of gravitational waves (GW) before annihilating. As such, they provide an interpretation for the recent signal reported by Pulsar Timing Array (PTA) collaborations. A related important question is whether such a scenario also leads to significant production of Primordial Black Holes (PBH).We investigate both GW and PBH production using 3D numerical simulations in an expanding background, with box sizes up to N = 3240,including the annihilation phase. We find that:i) the network decays exponentially, i.e. the false vacuum volume drops as ∼ exp(-η 3), with η the conformal time;ii) the GW spectrum is larger than traditional estimates by more than one order of magnitude, due to a delay between DW annihilation and the sourcing of GWs. We then present a novel semi-analytical method to estimate the PBH abundances: rare false vacuum pockets of super-Hubble size collapse to PBHs if their energy density becomes comparable to the background when they cross the Hubble scale. Smaller (but more abundant) pockets will instead collapse only if they are close to spherical. This introduces very large uncertainties in the final PBH abundance. The first phenomenological implication is that the DW interpretation of the PTA signal is compatible with observational constraints on PBHs, within the uncertainties. Second, in a different parameter region, the dark matter can be entirely in the form of asteroid-mass PBHs from the DW collapse. Remarkably, this would also lead to a GW background in the observable range of LIGO-Virgo-KAGRA and future interferometers, such as LISA and Einstein Telescope.

Fuzzy‐PID controller based on improved LFPSO for temperature and humidity control in a CA ripening system

AbstractTemperature and humidity as the key factors affecting the storage and ripening of fruits and vegetables directly determine the quality of fruits and vegetables. In this paper, a temperature and humidity model was constructed based on an integrated device of controlled atmosphere storage and ripening. Aiming at the shortcomings of the temperature and humidity model such as large inertia, nonlinearity, and model uncertainty, a fuzzy‐PID controller based on the improved Lévy flight particle swarm algorithm (LFPSO) is proposed. Initially, a mathematical model of temperature and humidity is developed through mechanism research and parameter estimation. Subsequently, a temperature and humidity fuzzy‐PID control strategy is proposed for regulating temperature and humidity. An improved LFPSO is then introduced to optimize the key parameters of the fuzzy‐PID controller, such as the quantization factor and the scale factor. The superiority of the improved LFPSO algorithm is verified by comparing the test functions. Finally, the improved LFPSO‐fuzzy‐PID controller, fuzzy‐PID controller, and Smith‐PID controller are applied to the simulation model for comparison using the MATLAB Simulink simulation platform. The results show that the improved LFPSO‐fuzzy‐PID control algorithm has a good control effect in the temperature and humidity simulation system of controlled atmosphere (CA) ripening container, which provides a reference for solving the optimization problem of actual engineering design.Practical applicationsIn order to reduce the postharvest cold chain losses of fruits and vegetables, this paper proposes a containerized style device that combines fruits and vegetables storage and ripening for simplifying the losses caused by transshipment at cold chain nodes. Since temperature and humidity play a key role in fruits and vegetables storage and ripening, which directly affect the product quality, this paper establishes a complete mathematical model of CA ripening temperature and humidity system by combining mechanism modeling and parameter identification. In order to keep the temperature and humidity within the ideal range, Smith‐PID controller, fuzzy‐PID controller, and fuzzy‐PID control method based on improved Lévy flight particle swarm optimization are proposed to regulate the air conditioner and humidifier. The performance of three different types of controllers was tested on the MATLAB. The simulation results demonstrate that the improved LFPSO‐fuzzy‐PID controller is superior and more effective than Smith‐PID and fuzzy‐PID controllers.

Robustness of LiDAR-assisted controller towards measurement uncertainty

Many studies have concluded that LiDAR-assisted control can be used to increase annual energy production (AEP) as well as reduce extreme loads and fatigue damage on blades, tower, and main bearing components. However, most studies assume perfect LiDAR measurements and do not consider large measurement uncertainties on LiDAR measurements. In this study we evaluate the potential benefits in terms of fatigue load reduction from using a simple robust feedforward controller. In addition, we evaluate the impact of measurement uncertainties exemplified by a time shift between the expected time the wind hits the rotor and the actual time it hits the rotor as well as a mean wind speed measurement bias. Results shows that fatigue loads can be reduced up to 7% on the tower sections and up to 4% on other main components. However, these results only applies when the LiDAR is assumed to give perfect information about what is hitting the rotor plane. The load reduction degrades with the magnitude of a time shift of the arriving LiDAR wind measurements. A delay up to 2.5 seconds still gives reasonable load reductions. Furthermore, a positive wind speed bias does not affect the performance of the LiDAR-assisted controller, whereas a negative bias significantly deteriorates performance.

Downstream natural gas composition across U.S. and Canada: implications for indoor methane leaks and hazardous air pollutant exposures

Previous research has shown that natural gas (NG) leaks from residential appliances are common, affecting greenhouse gas emission inventories and indoor air quality. To study these implications, we collected and analyzed 587 unburned NG samples from 481 residences over 17 North American cities for hydrocarbons, hazardous air pollutants, and organosulfur odorants. Nearly all (97% of) gas samples contained benzene (between-city mean: 2335 ppbv [95% CI: 2104, 2607]) with substantial variability between cities. Vancouver, Los Angeles, Calgary, and Denver had at least 2x higher mean benzene concentrations than other cities sampled, with Vancouver exhibiting a nearly 50x greater mean benzene level than the lowest-concentration city (Boston). We estimate that current U.S. and Canadian emissions inventories are missing an additional 25 000 [95% CI: 19 000, 34 000] and 4000 [95% CI: 3700, 5200] lbs benzene yr−1 through downstream NG leakage, respectively. Concentrations of odorants added for leak detection varied substantially across cities, indicating a lack of standardization. Houston, for instance, had 5x higher mean tert-butyl mercaptan levels than Toronto. Using these odorant measurements, we found that methane emissions as high as 0.0080–0.28 g h−1 and indoor benzene enhancements 0.0096–0.11 ppbv could go undetected by persons with an average sense of smell, with large uncertainties driven by smelling sensitivity, gas composition, and household conditions. We also observed larger leaks (>10 ppm ambient methane) in ∼4% of surveyed homes, confirming that indoor leakage occurs at varying degrees despite the presence of odorants. Overall, our results illustrate the importance of downstream NG composition to understand potential emissions, exposures, and odor-mediated leak detection levels. Given methane’s global warming potency, benzene’s toxicity, and wide variation in smelling abilities, our findings highlight the deficiencies regarding the sole reliance on odorization to alert and protect all occupants from indoor leaks.

Comparison of linear energy transfer measurement for therapeutic carbon beam using CR-39 and TLD

The measurement of linear energy transfer (LET) is crucial for the evaluation of the radiation effect in heavy ion therapy. As two detectors which are convenient to implant into the phantom, the performance of CR-39 and thermoluminescence detector (TLD) for LET measurement was compared by experiment and simulation in this study. The results confirmed the applicability of both detectors for LET measurements, but also revealed that the CR-39 detector would lead to potential overestimation of dose-averaged LET compared with the simulation by PHITS, while the TLD would have a large uncertainty measuring ions with LET larger than 20 keV μm−1. The results of this study were expected to improve the detection method of LET for therapeutic carbon beam and would finally be benefit to the quality assurance of heavy ion radiotherapy.

Climate mitigation potential of cover crops in the United States is regionally concentrated and lower than previous estimates.

Widespread adoption of regenerative agriculture practices is an integral part of the US plan to achieve net-zero greenhouse gas emissions by 2050. National incentives have particularly increased for the adoption of cover crops (CCs), which have presumably large carbon (C) sequestration potential. However, assessments of national CC climate benefits have not fully considered regional variability, changing C sequestration rates over time, and potential N2O trade-offs. Using the DayCent soil biogeochemical model and current national survey data, we estimate CC climate change mitigation potential to be 39.0 ± 24.1 Mt CO2e year-1, which is 45%-65% lower than previous estimates, with large uncertainty attributed to N2O impacts. Three-fourths of this climate change mitigation potential is concentrated in the North Central, Southern Great Plains and Lower Mississippi regions. Public investment should be focused in these regions to maximize CC climate benefits, but the national contribution of CC to emissions targets may be lower than previously anticipated.

The impacts of climate change on offshore wind park projects: from changes in resource to project profitability

Offshore wind power generation is projected to increase 15-fold over the next two decades. This generation is, however, weather dependent. The compounding of the anthropogenic climate change signal with high spatial and temporal wind variability can lead to large uncertainties in the projected impacts of climate change on wind resources, and further down the line in the economics of a project. In this study, we showcase a methodology to analyze the impact of climate change on the economic indicators of an offshore wind farm project. Projections of changes in wind resources are obtained using an ensemble of statistically downscaled Coupled Model Intercomparison Project 6 (CMIP6) for different emission scenarios and time periods. A series of assumptions about the features of a representative wind farm and its key economic parameters are made to compute two economic indicators: the Internal Rate of Return (IRR) and the Levelized Cost of Energy (LCOE). The IRR is an estimate of the profitability of potential investments and the LCOE is the cost over the lifetime of an investment compared to the expected energy production. We find that the effect of changes of resource on IRR and LCOE depends on the region, emissions scenario, and projection period. In general, and conditioned on the assumptions underlying this study, impacts on IRR are more significant -i.e., with differences between -0.5% and -1% in Brazil- than on LCOE. We conclude that it is especially important to take into account climate change when making investment decisions based on a project’s expected profitability.

Object and person tracking systems to enable extremity dosimetry in nuclear medicine using computational methods

Nuclear medicine (NM) professionals are potentially exposed to high doses of ionising radiation, particularly in the skin of the hands. Ring dosimeters are used by the workers to ensure extremity doses are kept below the legal limits. However, ring dosimeters are often susceptible to large uncertainties, so it is difficult to ensure a correct measurement using the traditional occupational monitoring methods. An alternative solution is to calculate the absorbed dose by using Monte Carlo simulations. This method could reduce the uncertainty in dose calculation if the exact positions of the worker and the radiation source are represented in these simulations. In this study we present a set of computer vision and artificial intelligence algorithms that allow us to track the exact position of unshielded syringes and the hands of NM workers. We showcase a possible hardware configuration to acquire the necessary input data for the algorithms. And finally, we assess the tracking confidence of our software. The tracking accuracy achieved for the syringe detection was 57% and for the hand detection 98%.