Relation between Double Layer Structure, Capacitance, and Surface Tension in Electrowetting of Graphene and Aqueous Electrolytes.

Deciphering the mechanisms of charge storage on carbon-based materials is pivotal for the development of next-generation electrochemical energy storage systems. Graphene, the building block of graphitic electrodes, is an ideal model for probing such processes on a fundamental level. Herein, we investigate the thermodynamics of the graphene/aqueous electrolyte interface by utilizing a multiscale quantum mechanics-classical molecular dynamics (QM/MD) approach to provide insights into the effect of alkali metal ion (Li+) concentration on the interfacial tension (γSL) of the charged graphene/electrolyte interface. We demonstrate that the dependence of γSL on the applied surface charge exhibits an asymmetric behavior relative to the neutral surface. At the positively charged graphene sheet, the electrowetting response is amplified by electrolyte concentration, resulting in a strongly hydrophilic surface. On the contrary, at negative potential bias, γSL shows a weaker response to the charging of the electrode. Changes in γSL greatly affect the total areal capacitance predicted by the Young-Lippmann equation but have a negligible impact on the simulated total areal capacitance, indicating that the EDL structure is not directly correlated with the wettability of the surface and different interfacial mechanisms drive the two phenomena. The proposed model is validated experimentally by studying the electrowetting response of highly oriented pyrolytic graphite over a wide range of electrolyte concentrations. Our work presents the first combined theoretical and experimental study on electrowetting using carbon surfaces, introducing new conceptual routes for the investigation of wetting phenomena under potential bias.

Web-CONEXS: an inroad to theoretical X-ray absorption spectroscopy.

Accurate analysis of the rich information contained within X-ray spectra usually calls for detailed electronic structure theory simulations. However, density functional theory (DFT), time-dependent DFT and many-body perturbation theory calculations increasingly require the use of advanced codes running on high-performance computing (HPC) facilities. Consequently, many researchers who would like to augment their experimental work with such simulations are hampered by the compounding of nontrivial knowledge requirements, specialist training and significant time investment. To this end, we present Web-CONEXS, an intuitive graphical web application for democratizing electronic structure theory simulations. Web-CONEXS generates and submits simulation workflows for theoretical X-ray absorption and X-ray emission spectroscopy to a remote computing cluster. In the present form, Web-CONEXS interfaces with three software packages: ORCA, FDMNES and Quantum ESPRESSO, and an extensive materials database courtesy of the Materials Project API. These software packages have been selected to model diverse materials and properties. Web-CONEXS has been conceived with the novice user in mind; job submission is limited to a subset of simulation parameters. This ensures that much of the simulation complexity is lifted and preliminary theoretical results are generated faster. Web-CONEXS can be leveraged to support beam time proposals and serve as a platform for preliminary analysis of experimental data.

Design and Implementation of a National SARS-CoV-2 Monitoring Program in England: REACT-1 Study.

Data System. The REal-time Assessment of Community Transmission-1 (REACT-1) Study was funded by the Department of Health and Social Care in England to provide reliable and timely estimates of prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection over time, by person and place. Data Collection/Processing. The study team (researchers from Imperial College London and its logistics partner Ipsos) wrote to named individuals aged 5 years and older in random cross-sections of the population of England, using the National Health Service list of patients registered with a general practitioner (near-universal coverage) as a sampling frame. We collected data over 2 to 3 weeks approximately every month across 19 rounds of data collection from May 1, 2020, to March 31, 2022. Data Analysis/Dissemination. We have disseminated the data and study materials widely via the study Web site, preprints, publications in peer-reviewed journals, and the media. We make available data tabulations, suitably anonymized to protect participant confidentiality, on request to the study's data access committee. Public Health Implications. The study provided inter alia real-time data on SARS-CoV-2 prevalence over time, by area, and by sociodemographic variables; estimates of vaccine effectiveness; and symptom profiles, and detected emergence of new variants based on viral genome sequencing. (Am J Public Health. 2023;113(5):545-554. https://doi.org/10.2105/AJPH.2023.307230).

Constant chemical potential-quantum mechanical-molecular dynamics simulations of the graphene-electrolyte double layer.

We present the coupling of two frameworks-the pseudo-open boundary simulation method known as constant potential molecular dynamics simulations (CµMD), combined with quantum mechanics/molecular dynamics (QMMD) calculations-to describe the properties of graphene electrodes in contact with electrolytes. The resulting CµQMMD model was then applied to three ionic solutions (LiCl, NaCl, and KCl in water) at bulk solution concentrations ranging from 0.5 M to 6 M in contact with a charged graphene electrode. The new approach we are describing here provides a simulation protocol to control the concentration of electrolyte solutions while including the effects of a fully polarizable electrode surface. Thanks to this coupling, we are able to accurately model both the electrode and solution side of the double layer and provide a thorough analysis of the properties of electrolytes at charged interfaces, such as the screening ability of the electrolyte and the electrostatic potential profile. We also report the calculation of the integral electrochemical double layer capacitance in the whole range of concentrations analyzed for each ionic species, while the quantum mechanical simulations provide access to the differential and integral quantum capacitance. We highlight how subtle features, such as the adsorption of potassium graphene or the tendency of the ions to form clusters contribute to the ability of graphene to store charge, and suggest implications for desalination.

A regional nuclear conflict would compromise global food security.

A limited nuclear war between India and Pakistan could ignite fires large enough to emit more than 5 Tg of soot into the stratosphere. Climate model simulations have shown severe resulting climate perturbations with declines in global mean temperature by 1.8 °C and precipitation by 8%, for at least 5 y. Here we evaluate impacts for the global food system. Six harmonized state-of-the-art crop models show that global caloric production from maize, wheat, rice, and soybean falls by 13 (±1)%, 11 (±8)%, 3 (±5)%, and 17 (±2)% over 5 y. Total single-year losses of 12 (±4)% quadruple the largest observed historical anomaly and exceed impacts caused by historic droughts and volcanic eruptions. Colder temperatures drive losses more than changes in precipitation and solar radiation, leading to strongest impacts in temperate regions poleward of 30°N, including the United States, Europe, and China for 10 to 15 y. Integrated food trade network analyses show that domestic reserves and global trade can largely buffer the production anomaly in the first year. Persistent multiyear losses, however, would constrain domestic food availability and propagate to the Global South, especially to food-insecure countries. By year 5, maize and wheat availability would decrease by 13% globally and by more than 20% in 71 countries with a cumulative population of 1.3 billion people. In view of increasing instability in South Asia, this study shows that a regional conflict using <1% of the worldwide nuclear arsenal could have adverse consequences for global food security unmatched in modern history.

Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change.

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate-related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process-based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.

Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people.

BACKGROUND: Rapid detection, isolation, and contact tracing of community COVID-19 cases are essential measures to limit the community spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to identify a parsimonious set of symptoms that jointly predict COVID-19 and investigated whether predictive symptoms differ between the B.1.1.7 (Alpha) lineage (predominating as of April 2021 in the US, UK, and elsewhere) and wild type. METHODS AND FINDINGS: We obtained throat and nose swabs with valid SARS-CoV-2 PCR test results from 1,147,370 volunteers aged 5 years and above (6,450 positive cases) in the REal-time Assessment of Community Transmission-1 (REACT-1) study. This study involved repeated community-based random surveys of prevalence in England (study rounds 2 to 8, June 2020 to January 2021, response rates 22%-27%). Participants were asked about symptoms occurring in the week prior to testing. Viral genome sequencing was carried out for PCR-positive samples with N-gene cycle threshold value < 34 (N = 1,079) in round 8 (January 2021). In univariate analysis, all 26 surveyed symptoms were associated with PCR positivity compared with non-symptomatic people. Stability selection (1,000 penalized logistic regression models with 50% subsampling) among people reporting at least 1 symptom identified 7 symptoms as jointly and positively predictive of PCR positivity in rounds 2-7 (June to December 2020): loss or change of sense of smell, loss or change of sense of taste, fever, new persistent cough, chills, appetite loss, and muscle aches. The resulting model (rounds 2-7) predicted PCR positivity in round 8 with area under the curve (AUC) of 0.77. The same 7 symptoms were selected as jointly predictive of B.1.1.7 infection in round 8, although when comparing B.1.1.7 with wild type, new persistent cough and sore throat were more predictive of B.1.1.7 infection while loss or change of sense of smell was more predictive of the wild type. The main limitations of our study are (i) potential participation bias despite random sampling of named individuals from the National Health Service register and weighting designed to achieve a representative sample of the population of England and (ii) the necessary reliance on self-reported symptoms, which may be prone to recall bias and may therefore lead to biased estimates of symptom prevalence in England. CONCLUSIONS: Where testing capacity is limited, it is important to use tests in the most efficient way possible. We identified a set of 7 symptoms that, when considered together, maximize detection of COVID-19 in the community, including infection with the B.1.1.7 lineage.

Large potential for crop production adaptation depends on available future varieties.

Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5-8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1-2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro-ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5-8.5. The results highlight that region-specific breeding efforts are required to allow for a successful adaptation to climate change.

COVID-19 mortality in the UK Biobank cohort: revisiting and evaluating risk factors.

Most studies of severe/fatal COVID-19 risk have used routine/hospitalisation data without detailed pre-morbid characterisation. Using the community-based UK Biobank cohort, we investigate risk factors for COVID-19 mortality in comparison with non-COVID-19 mortality. We investigated demographic, social (education, income, housing, employment), lifestyle (smoking, drinking, body mass index), biological (lipids, cystatin C, vitamin D), medical (comorbidities, medications) and environmental (air pollution) data from UK Biobank (N = 473,550) in relation to 459 COVID-19 and 2626 non-COVID-19 deaths to 21 September 2020. We used univariate, multivariable and penalised regression models. Age (OR = 2.76 [2.18-3.49] per S.D. [8.1 years], p = 2.6 × 10-17), male sex (OR = 1.47 [1.26-1.73], p = 1.3 × 10-6) and Black versus White ethnicity (OR = 1.21 [1.12-1.29], p = 3.0 × 10-7) were independently associated with and jointly explanatory of (area under receiver operating characteristic curve, AUC = 0.79) increased risk of COVID-19 mortality. In multivariable regression, alongside demographic covariates, being a healthcare worker, current smoker, having cardiovascular disease, hypertension, diabetes, autoimmune disease, and oral steroid use at enrolment were independently associated with COVID-19 mortality. Penalised regression models selected income, cardiovascular disease, hypertension, diabetes, cystatin C, and oral steroid use as jointly contributing to COVID-19 mortality risk; Black ethnicity, hypertension and oral steroid use contributed to COVID-19 but not non-COVID-19 mortality. Age, male sex and Black ethnicity, as well as comorbidities and oral steroid use at enrolment were associated with increased risk of COVID-19 death. Our results suggest that previously reported associations of COVID-19 mortality with body mass index, low vitamin D, air pollutants, renin-angiotensin-aldosterone system inhibitors may be explained by the aforementioned factors.

Temperature increase reduces global yields of major crops in four independent estimates.

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.

Predictive Accuracy of a Polygenic Risk Score-Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease.

Importance: The incremental value of polygenic risk scores in addition to well-established risk prediction models for coronary artery disease (CAD) is uncertain. Objective: To examine whether a polygenic risk score for CAD improves risk prediction beyond pooled cohort equations. Design, Setting, and Participants: Observational study of UK Biobank participants enrolled from 2006 to 2010. A case-control sample of 15 947 prevalent CAD cases and equal number of age and sex frequency-matched controls was used to optimize the predictive performance of a polygenic risk score for CAD based on summary statistics from published genome-wide association studies. A separate cohort of 352 660 individuals (with follow-up to 2017) was used to evaluate the predictive accuracy of the polygenic risk score, pooled cohort equations, and both combined for incident CAD. Exposures: Polygenic risk score for CAD, pooled cohort equations, and both combined. Main Outcomes and Measures: CAD (myocardial infarction and its related sequelae). Discrimination, calibration, and reclassification using a risk threshold of 7.5% were assessed. Results: In the cohort of 352 660 participants (mean age, 55.9 years; 205 297 women [58.2%]) used to evaluate the predictive accuracy of the examined models, there were 6272 incident CAD events over a median of 8 years of follow-up. CAD discrimination for polygenic risk score, pooled cohort equations, and both combined resulted in C statistics of 0.61 (95% CI, 0.60 to 0.62), 0.76 (95% CI, 0.75 to 0.77), and 0.78 (95% CI, 0.77 to 0.79), respectively. The change in C statistic between the latter 2 models was 0.02 (95% CI, 0.01 to 0.03). Calibration of the models showed overestimation of risk by pooled cohort equations, which was corrected after recalibration. Using a risk threshold of 7.5%, addition of the polygenic risk score to pooled cohort equations resulted in a net reclassification improvement of 4.4% (95% CI, 3.5% to 5.3%) for cases and -0.4% (95% CI, -0.5% to -0.4%) for noncases (overall net reclassification improvement, 4.0% [95% CI, 3.1% to 4.9%]). Conclusions and Relevance: The addition of a polygenic risk score for CAD to pooled cohort equations was associated with a statistically significant, yet modest, improvement in the predictive accuracy for incident CAD and improved risk stratification for only a small proportion of individuals. The use of genetic information over the pooled cohort equations model warrants further investigation before clinical implementation.

Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments.

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models, global agricultural economics models, site-based crop models and within-country regional economics models. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there are mixed areas of positive and negative simulated wheat and maize yield changes, with declines in some breadbasket regions, at both 1.5°C and 2.0°C. Declines are especially evident in simulations that do not take into account direct CO2 effects on crops. These projected global yield changes mostly resulted in increases in prices and areas of wheat and maize in two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on the region and the crop. In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan, resulted in an increase in vulnerable households and the poverty rate.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

Biophysical and economic implications for agriculture of +1.5° and +2.0°C global warming using AgMIP Coordinated Global and Regional Assessments.

This study presents results of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments (CGRA) of +1.5° and +2.0°C global warming above pre-industrial conditions. This first CGRA application provides multi-discipline, multi-scale, and multi-model perspectives to elucidate major challenges for the agricultural sector caused by direct biophysical impacts of climate changes as well as ramifications of associated mitigation strategies. Agriculture in both target climate stabilizations is characterized by differential impacts across regions and farming systems, with tropical maize Zea mays experiencing the largest losses, while soy Glycine max mostly benefits. The result is upward pressure on prices and area expansion for maize and wheat Triticum aestivum, while soy prices and area decline (results for rice Oryza sativa are mixed). An example global mitigation strategy encouraging bioenergy expansion is more disruptive to land use and crop prices than the climate change impacts alone, even in the +2.0°C scenario which has a larger climate signal and lower mitigation requirement than the +1.5°C scenario. Coordinated assessments reveal that direct biophysical and economic impacts can be substantially larger for regional farming systems than global production changes. Regional farmers can buffer negative effects or take advantage of new opportunities via mitigation incentives and farm management technologies. Primary uncertainties in the CGRA framework include the extent of CO2 benefits for diverse agricultural systems in crop models, as simulations without CO2 benefits show widespread production losses that raise prices and expand agricultural area.

Evaluating the utility of dynamical downscaling in agricultural impacts projections.

Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling--nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output--to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections.

Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison.

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.

Constraints and potentials of future irrigation water availability on agricultural production under climate change.

We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400-1,400 Pcal (8-24% of present-day total) when CO2 fertilization effects are accounted for or 1,400-2,600 Pcal (24-43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20-60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600-2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.

Climate change effects on agriculture: economic responses to biophysical shocks.

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Multisectoral climate impact hotspots in a warming world.

The impacts of global climate change on different aspects of humanity's diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 °C above the 1980-2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 °C. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty.

A spatial modeling framework to evaluate domestic biofuel-induced potential land use changes and emissions.

We present a novel bottom-up approach to estimate biofuel-induced land-use change (LUC) and resulting CO2 emissions in the U.S. from 2010 to 2022, based on a consistent methodology across four essential components: land availability, land suitability, LUC decision-making, and induced CO2 emissions. Using high-resolution geospatial data and modeling, we construct probabilistic assessments of county-, state-, and national-level LUC and emissions for macroeconomic scenarios. We use the Cropland Data Layer and the Protected Areas Database to characterize availability of land for biofuel crop cultivation, and the CERES-Maize and BioCro biophysical crop growth models to estimate the suitability (yield potential) of available lands for biofuel crops. For LUC decision-making, we use a county-level stochastic partial-equilibrium modeling framework and consider five scenarios involving annual ethanol production scaling to 15, 22, and 29 BG, respectively, in 2022, with corn providing feedstock for the first 15 BG and the remainder coming from one of two dedicated energy crops. Finally, we derive high-resolution above-ground carbon factors from the National Biomass and Carbon Data set to estimate emissions from each LUC pathway. Based on these inputs, we obtain estimates for average total LUC emissions of 6.1, 2.2, 1.0, 2.2, and 2.4 gCO2e/MJ for Corn-15 Billion gallons (BG), Miscanthus × giganteus (MxG)-7 BG, Switchgrass (SG)-7 BG, MxG-14 BG, and SG-14 BG scenarios, respectively.

Implementation of a mixed-methods heat acclimation programme in a professional soccer referee before the 2022 FIFA world cup in Qatar: a case study.

Objectives: This case study reports the real-world practicalities of implementing a mixed-methods heat acclimation (HA) programme before the 2022 FIFA World Cup. Methods: One male English soccer referee (age: 44 years; height: 1.82 m; body mass: 76.0 kg) who had officiated professionally for over 17 years and had over 10 years' experience officiating in European and international matches undertook an 11-session HA programme over 22 days. On days 1 and 22, a 30 min fixed-intensity heat tolerance test (9 km.h-1, 2% gradient, 40°C, 40% relative humidity) was performed, and physiological and perceptual responses were measured. A mixed-methods HA approach was used, including environmental chamber isothermic training, post-temperate training saunas and hot water immersion. Results: Compared with the pre-test, peak core temperature reduced by 0.40°C (38.4 vs 38.0°C; minimal detectable change (MDC) = 0.34°C), peak skin temperature reduced by 0.5°C (36.7 vs 36.2°C; MDC=0.28°C) and peak heart rate reduced by 5 b·min-1 (167 vs. 162 b·min-1; MDC=4 b·min-1) in the post-test. In the post-test, the sweat rate increased by 17% (1.94 vs 2.27 L.h-1; MDC=0.42 L.h-1). Peak thermal sensation (7 = 'hot') and the rating of perceived exertion (3 = 'moderate') were unchanged between the tests. However, peak thermal comfort (3 = 'slightly uncomfortable' vs 2 = 'uncomfortable') was rated lower in the post-test. Conclusion: The HA programme elicited positive physiological but indifferent perceptual responses, highlighting that mixed-methods HA can be implemented when a referee still has officiating, travel and training responsibilities during the HA window.