Search

Background Staphylococcus aureus bacteremia (SAB) disproportionately affects Black patients. The reasons for this disparity are unclear. Methods We evaluated a prospectively ascertained cohort of patients with SAB from 1995 to 2020. Clinical characteristics, bacterial genotypes, and outcome were compared among Black and White patients with SAB. Multivariable logistic regression models were used to determine factors independently associated with the outcomes. Results Among 3068 patients with SAB, 1107 (36%) were Black. Black patients were younger (median, 56 years vs 63 years; P < .001) and had higher rates of diabetes (47.5% vs 34.5%, P < .001), hemodialysis dependence (40.0% vs 7.3%, P < .001), and human immunodeficiency virus (6.4% vs 0.6%, P < .001). Black patients had higher rates of methicillin-resistant S. aureus (49.3% vs 44.9%, P = .020), including the USA300 hypervirulent clone (11.5% vs 8.4%, P = .007). White patients had higher rates of corticosteroid use (22.4% vs 15.8%, P < .0001) and surgery in the preceding 30 days (28.1% vs 18.7%, P < .001). Although the median Acute Physiology Score (APS) at the time of initial SAB diagnosis was significantly higher in Black patients (median APS, 9; interquartile range [IQR], 5–14 vs median APS, 7; IQR, 4–12; P < .001), race was not associated with 90-day mortality (risk ratio, 1.02; 95% confidence interval, .93–1.12), and rates of metastatic infection were lower among Black patients (37.2% vs 41.3% White, P = .029). Conclusions Despite differences in Black patients' higher APS on presentation and more risk factors, including a 5 times higher risk of hemodialysis dependence, 90-day mortality among Black and White patients with SAB was similar.

High-quality, long-time-series measurements of atmospheric greenhouse gases show interannual variability in the measured seasonal cycles. These changes can be analyzed to better understand the carbon cycle and the impact of climate drivers. However, nearly all discrete measurement records contain gaps and have noise due to the influence of local fluxes or synoptic variability. To facilitate analysis, filtering and curve-fitting techniques are often applied to these time series. Previous studies have recognized that there is an inherent uncertainty associated with this curve fitting, and the choice of a given mathematical method might introduce biases. Since uncertainties are seldom propagated to the metrics under study, this can lead to misinterpretation of the signal. In this study, we use an ensemble-based approach to quantify the uncertainty of the derived seasonal cycle metrics. We apply it to CO2 dry-air mole fraction time series from flask measurements in the Northern Hemisphere. We use this ensemble-based approach to analyze the carbon uptake period (CUP: the time of the year when the CO2 uptake is greater than the CO2 release): its onset, termination and duration. Previous studies have diagnosed CUP based on the dates on which the detrended, zero-centered seasonal cycle curve switches from positive to negative (the downward zero-crossing date, DZCD) and vice versa (upward zero-crossing date, UZCD). However, the UZCD is sensitive to the skewness of the CO2 seasonal cycle during the net carbon release period. Hence, we develop an alternative method proposed by Barlow et al. (2015) to estimate the onset and termination of the CUP based on a threshold defined in terms of the first derivative of the CO2 seasonal cycle. Using the ensemble approach, we arrive at a tighter constraint to the threshold by considering the annual uncertainty; we call this the ensemble of first derivative (EFD) method. Further, using the EFD approach and an additional curve-fitting algorithm, we show that (a) the uncertainty of the studied metrics is smaller using the EFD method than when approximated using the timing of the zero-crossing date (ZCD), and (b) the onset and termination dates derived with the EFD method provide more robust results, irrespective of the curve-fitting method applied to the data.

Do children, like most adults, believe that only kin and close others are obligated to help oneanother? In two studies (total N = 1140), we examined whether children (~5- to ~10-yos) andadults across five different societies consider social relationship when ascribing prosocialobligations. Contrary to the view that such discriminations are a natural default in humanreasoning, younger children in the United States (Studies 1 and 2) and across cultures (Study 2)generally judged everyone—parents, friends, and strangers—as obligated to help someone-inneed.Older children and adults, on the other hand, tended to exhibit more discriminant judgments.They considered parents more obligated to help than friends followed by strangers—though thiseffect was stronger in some cultures than others. Our findings suggest that children’s initial senseof prosocial obligation in social-relational contexts starts out broad and generally becomes moreselective over the course of development.

An appropriate representation of point source emissions in atmospheric transport models is very challenging. In the Stochastic Time-Inverted Lagrangian Transport model (STILT), all point source emissions are typically released from the surface, meaning that the actual emission stack height plus subsequent plume rise is not considered. This can lead to erroneous predictions of trace gas concentrations, especially during nighttime when vertical atmospheric mixing is minimal. In this study we use two Weather Research and Forecasting (WRF)–STILT model approaches to simulate fossil fuel CO2 (ffCO2) concentrations: (1) the standard “surface source influence (SSI)” approach and (2) an alternative “volume source influence (VSI)” approach where nearby point sources release CO2 according to their effective emission height profiles. The comparison with 14C-based measured ffCO2 data from 2-week integrated afternoon and nighttime samples collected at Heidelberg, 30 m above ground level shows that the root-mean-square deviation (RMSD) between modelled and measured ffCO2 is indeed almost twice as high during the night (RMSD =6.3 ppm) compared to the afternoon (RMSD =3.7 ppm) when using the standard SSI approach. In contrast, the VSI approach leads to a much better performance at nighttime (RMSD =3.4 ppm), which is similar to its performance during afternoon (RMSD =3.7 ppm). Representing nearby point source emissions with the VSI approach could thus be a first step towards exploiting nocturnal observations in STILT. The ability to use nighttime observations in atmospheric inversions would dramatically increase the observational data and allow for the investigation of different source mixtures or diurnal cycles. To further investigate the differences between these two approaches, we conducted a model experiment in which we simulated the ffCO2 contributions from 12 artificial power plants with typical annual emissions of 1 million tonnes of CO2 and with distances between 5 and 200 km from the Heidelberg observation site. We find that such a power plant must be more than 50 km away from the observation site in order for the mean modelled ffCO2 concentration difference between the SSI and VSI approach to fall below 0.1 ppm during situations with low mixing heights smaller than 500 m.

Atmospheric CO2 measurements from background sites across the Northern Hemisphere have been used to study the changes in the carbon uptake period (CUP) i.e., when plants are able to grow and assimilate carbon from the atmosphere. Previous studies that use CO2 dry air mole fraction data diagnosed CUP using zero-crossing dates (ZCD, when the detrended seasonal cycle switches from positive to negative sign and vice versa). The CUP can also be estimated using the first derivative of the CO2 seasonal cycle. In previous work we show that applying the first derivative method to an ensemble of fitted CO2 mole fraction curves provides better constraints to the CUP by considering year-to-year uncertainty in estimates across the ensemble members. We call this the ensemble of first derivative method (EFD method). In addition to curve fitting uncertainty and year-to-year flux variability, atmospheric transport might explain a significant portion of observed CO2 variations at various surface stations, affecting the interpretation of the CUP and similar metrics.Hence, in this study we examine how atmospheric transport of fluxes, and spatial variations in the start and ending dates of carbon uptake, smooth the signal in atmospheric CO2 and affect the CUP estimates when using remote background observation sites to interpret actual fluxes. We use a synthetic data experiment where idealized NEE fluxes are transported forward (with atmospheric transport model TM3 (Heimann and Körner, 2003) and fixed year meteorology) and the atmospheric concentrations are sampled at the location of the measurement sites. A fixed year from the Jena CarboScope Inversion (Rödenbeck et al., 2003, doi:10.17871/CarboScope-sEXTocNEET_v2022) was used to generate an idealized NEE flux time series with no interannual variability in the CUP at any given pixel. Then, we prescribe changes in the CUP of NEE flux to Northern Hemisphere land pixels with clear seasonal cycles and evaluate the accuracy of the ZCD and EFD methods in capturing this known change from CUP in the surface fluxes, from the resulting CO2 mixing ratio obtained from the forward transport run.We find that CUP changes estimated by both EFD and ZCD based on CO2 measurements are smaller by a factor of 2-4 than the perturbations applied in NEE space, and that the EFD method is more sensitive to surface CUP changes than the ZCD. This " dampening" factor varies across sites, depending on the mixing of spatially varying NEE signals with differing CUP timing which integrate to a reduced atmospheric expression of CUP. We further analyse the contribution of 1) atmospheric transport by comparing simulation that uses inter annually varying meteorology 2) different TransCom-3 regions to CUP variations by selectively manipulating NEE flux from a region and repeating the experiment.References:Heimann, H. and Körner, S. (2003). The global atmospheric tracer model tm3. Technical Reports- Max-Planck-Institut f ̈ur Biogeochemie 5, 5:131.Rödenbeck, C., Houweling, S., Gloor, M., and Heimann, M. (2003). Co2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport. Atmospheric Chemistry and Physics, 3(6):1919–1964. doi: 10.17871/CarboScope-sEXTocNEET_v2022.

Various satellite imagers of the vertically integrated column of carbon dioxide (XCO2) are under development to enhance the capabilities for the monitoring of fossil fuel (FF) CO2 emissions. XCO2 images can be used to detect plumes from cities and large industrial plants and to quantify the corresponding emission using atmospheric inversions techniques. However, this potential and the ability to catch the signal from more diffuse FF CO2 sources can be hampered by the mix between these FF signals and a background signal from other types of CO2 surface fluxes, and in particular of biogenic CO2 fluxes. The deployment of dense ground-based air-sampling networks for CO2 and radiocarbon (14CO2) could complement the spaceborne imagery by supporting the separation between the fossil fuel and biogenic or biofuel (BF) CO2 signals. We evaluate this potential complementarity with a high-resolution analytical inversion system focused on northern France, western Germany, Belgium, Luxembourg, and a part of the Netherlands and with pseudo-data experiments. The inversion system controls the FF and BF emissions from the large urban areas and plants, in addition to regional budgets of more diffuse emissions or of biogenic fluxes (NEE, net ecosystem exchange), at an hourly scale over a whole day. The system provides results corresponding to the assimilation of pseudo-data from a single track of a 300 km swath XCO2 imager at 2 km resolution and from surface ground-based CO2 and/or 14CO2 networks. It represents the diversity of 14CO2 sources and sinks and not just the dilution of radiocarbon-free FF CO2 emissions. The uncertainty in the resulting FF CO2 emissions at local (urban area/plant) to regional scales is directly derived and used to assess the potential of the different combinations of observation systems. The assimilation of satellite observations yields estimates of the morning regional emissions with an uncertainty down to 10 % (1σ) in the satellite field of view, from an assumed uncertainty of 15 % in the prior estimates. However, it does not provide direct information about emissions outside the satellite field of view or about afternoon or nighttime emissions. The co-assimilation of 14CO2 and CO2 surface observations leads to a further reduction of the uncertainty in the estimates of FF emissions. However, this further reduction is significant only in administrative regions with three or more 14CO2 and CO2 sampling sites. The uncertainty in the estimates of 1 d emission in North Rhine-Westphalia, a region with three sampling sites, decreases from 8 % to 6.6 % when assimilating the in situ 14CO2 and CO2 data in addition to the satellite data. Furthermore, this additional decrease appears to be larger when the ground stations are close to large FF emission areas, providing an additional direct constraint for the estimate of these sources rather than supporting the characterization of the background signal from the NEE and its separation from that of the FF emissions. More generally, the results indicate no amplification of the potential of each observation subsystem when they are combined into a large observation system with satellite and surface data.

Power plants and large industrial facilities contribute more than half of global anthropogenic CO2 emissions. Quantifying the emissions of these point sources is therefore one of the main goals of the planned constellation of anthropogenic CO2 monitoring satellites (CO2M) of the European Copernicus program. Atmospheric transport models may be used to study the capabilities of such satellites through observing system simulation experiments and to quantify emissions in an inverse modeling framework. How realistically the CO2 plumes of power plants can be simulated and how strongly the results may depend on model type and resolution, however, is not well known due to a lack of observations available for benchmarking. Here, we use the unique data set of aircraft in situ and remote sensing observations collected during the CoMet (Carbon Dioxide and Methane Mission) measurement campaign downwind of the coal-fired power plants at Bełchatów in Poland and Jänschwalde in Germany in 2018 to evaluate the simulations of six different atmospheric transport models. The models include three large-eddy simulation (LES) models, two mesoscale numerical weather prediction (NWP) models extended for atmospheric tracer transport, and one Lagrangian particle dispersion model (LPDM) and cover a wide range of model resolutions from 200 m to 2 km horizontal grid spacing. At the time of the aircraft measurements between late morning and early afternoon, the simulated plumes were slightly (at Jänschwalde) to highly (at Bełchatów) turbulent, consistent with the observations, and extended over the whole depth of the atmospheric boundary layer (ABL; up to 1800 m a.s.l. (above sea level) in the case of Bełchatów). The stochastic nature of turbulent plumes puts fundamental limitations on a point-by-point comparison between simulations and observations. Therefore, the evaluation focused on statistical properties such as plume amplitude and width as a function of distance from the source. LES and NWP models showed similar performance and sometimes remarkable agreement with the observations when operated at a comparable resolution. The Lagrangian model, which was the only model driven by winds observed from the aircraft, quite accurately captured the location of the plumes but generally underestimated their width. A resolution of 1 km or better appears to be necessary to realistically capture turbulent plume structures. At a coarser resolution, the plumes disperse too quickly, especially in the near-field range (0–8 km from the source), and turbulent structures are increasingly smoothed out. Total vertical columns are easier to simulate accurately than the vertical distribution of CO2, since the latter is critically affected by profiles of vertical stability, especially near the top of the ABL. Cross-sectional flux and integrated mass enhancement methods applied to synthetic CO2M data generated from the model simulations with a random noise of 0.5–1.0 ppm (parts per million) suggest that emissions from a power plant like Bełchatów can be estimated with an accuracy of about 20 % from single overpasses. Estimates of the effective wind speed are a critical input for these methods. Wind speeds in the middle of the ABL appear to be a good approximation for plumes in a well-mixed ABL, as encountered during CoMet.

The sabotage of the Nord Stream pipelines on 26 September 2022 led to the largest event of methane venting to the atmosphere on record. The pipelines contained about 300 000 tonnes of methane, and an estimate based on Europe's ICOS network quantified the emissions to the atmosphere at 90 000-300 000 tonnes of methane in the first days of the event (Ramonet et al., 2022). The vast majority of methane that vented from the pipelines into the water likely escaped to the atmosphere near-instantly via bubble transport, which had largely ceased by 1 October 2022. However, a fraction dissolved into the water. To investigate the possibility of a "long tail" of release of this dissolved methane to the atmosphere, we conducted airborne surveys of the leak area on 5 October 2022. Methane data were recorded with a Picarro G2401m onboard the HELiPOD platform, a drag probe attached to a helicopter with a rope, down to 30 m above sea level. We observed methane enhancements of up to 300 ppb above atmospheric background, in an area about 30 km both up- and downwind of the leak locations. Using an inverse model of atmospheric transport, we show that the atmospheric data can be explained by an area source and estimate a source strength on the order of tens of tonnes of methane per hour on the day of observations. To better understand the spatial distribution of the emissions, especially emissions upwind of the leak locations, we further run a model of oceanic transport for tracers released at the leak locations and couple it to a Wanninkhof-model of diffusive emissions. The areal emission distribution we find with this model has some similarities to the emission pattern retrieved using the airborne measurements. We conclude that a significant amount of methane was dissolved in the Baltic Sea during the outgassing event following the Nord Stream explosions. Methane that was initially dissolved in the surface layer still escaped to the atmosphere days after the initial outgassing event.

Methane ( CH4 ) is the second most important anthropogenic greenhouse gas, whose atmospheric concentration is modulated by human-induced activities, and it has a larger global warming potential than carbon dioxide ( CO2 ). Because of its short atmospheric lifetime relative to that of CO2 , the reduction of the atmospheric abundance of CH4 is an attractive target for short-term climate mitigation strategies. However, reducing the atmospheric CH4 concentration requires a reduction of its emissions and, therefore, knowledge of its sources. For this reason, the CO2 and Methane (CoMet) campaign in May and June 2018 assessed emissions of one of the largest CH4 emission hot spots in Europe, the Upper Silesian Coal Basin (USCB) in southern Poland, using top-down approaches and inventory data. In this study, we will focus on CH4 column anomalies retrieved from spectral radiance observations, which were acquired by the 1D nadir-looking passive remote sensing Methane Airborne MAPper (MAMAP) instrument, using the weighting-function-modified differential optical absorption spectroscopy (WFM-DOAS) method. The column anomalies, combined with wind lidar measurements, are inverted to cross-sectional fluxes using a mass balance approach. With the help of these fluxes, reported emissions of small clusters of coal mine ventilation shafts are then assessed. The MAMAP CH4 column observations enable an accurate assignment of observed fluxes to small clusters of ventilation shafts. CH4 fluxes are estimated for four clusters with a total of 23 ventilation shafts, which are responsible for about 40 % of the total CH4 mining emissions in the target area. The observations were made during several overflights on different days. The final average CH4 fluxes for the single clusters (or sub-clusters) range from about 1 to 9 t CH4 h−1 at the time of the campaign. The fluxes observed at one cluster during different overflights vary by as much as 50 % of the average value. Associated errors (1 σ ) are usually between 15 % and 59 % of the average flux, depending mainly on the prevailing wind conditions, the number of flight tracks, and the magnitude of the flux itself. Comparison to known hourly emissions, where available, shows good agreement within the uncertainties. If only emissions reported annually are available for comparison with the observations, caution is advised due to possible fluctuations in emissions during a year or even within hours. To measure emissions even more precisely and to break them down further for allocation to individual shafts in a complex source region such as the USCB, imaging remote sensing instruments are recommended.

Recent research has documented a small but significant correlation between psychopathic capacities and utilitarian moral judgment, although the findings are generally inconsistent and unclear. We propose that one way to make sense of mixed findings is to consider variation in perspective-taking capacities of psychopathic individuals. With this in mind, we had criminal offenders (n = 60), who varied in their psychopathy levels according to the Psychopathy Checklist-Revised (PCL-R), respond to common sacrificial moral dilemmas (e.g., trolley dilemmas) under different conditions. In a baseline condition, participants simply responded to the sacrificial moral dilemmas as is typically done in previous research. In an "emotion-salient" condition, participants had to reason about the emotions of another person after solving moral dilemmas (deliberative processing). In the "emotion-ambiguous" condition, participants saw images of people in distress, after solving moral dilemmas, but did not have to explicitly reason about such emotions (spontaneous processing). The four PCL-R facets predicted distinct interference effects depending on spontaneous versus deliberative processing of hypothetical victim's emotions. The findings suggest that the use of a multi-faceted approach to account for cognitive and moral correlates of psychopathy may help address previously mixed results. Implications and future directions for theory and research are discussed.

In an effort to combat COVID-19 and future pandemics, researchers have attempted to identify the factors underlying social distancing. Yet, much of this research relies on self-report measures. In two studies, we examine whether self-reported social distancing predicts objective distancing behavior. In Study 1, individuals’ self-reported social distancing predicted decreased mobility (assessed via smartphone step counts) during the COVID-19 pandemic. While participants high in self-reported distancing (+1 SD) exhibited a 33% reduction in daily step counts, those low in distancing (−1 SD) exhibited only a 3% reduction. Study 2 extended these findings to the group level. Self-reported social distancing at the U.S. state level accounted for 20% of the variance in states’ objective reduction in overall movement and visiting nonessential services (calculated via the GPS coordinates of ∼15 million people). Collectively, our results indicate that self-reported social distancing tracks actual social distancing behavior.

The coronavirus pandemic has had a significant influence on social interactions, introducing novel social norms such as mask-wearing and social distancing to protect people's health. Because these norms and associated practices are completely novel, it is unknown how children assess what kinds of interventions are appropriate under what circumstances and what principles they draw on in their decisions. We investigated children's reasoning about interventions against individuals who failed to adhere to COVID-19 norms. In this pre-registered study (N = 128), 4- to 7-year-olds heard stories about a norm violator, that is, a person who refuses to wear a mask in class (COVID condition) or wear indoor shoes in class when his or her shoes are muddy (Muddy Shoes condition). Children evaluated four different interventions-giving a mask/indoor shoes (Giving), preventing the person from entering (Exclusion), throwing a paper ball at the person (Throwing), and not intervening (Doing Nothing)-in terms of their rightness, niceness, and effectiveness. We found that across measures children evaluated Giving most positively, whereas they viewed Throwing most negatively. Doing Nothing and Exclusion received mixed evaluations across measures, revealing nuanced judgments of these interventions in children. In most measures, there was no difference between the COVID and Muddy Shoes conditions, suggesting that children's evaluations are not specific to the novel COVID-19 context. Together, our results show that children dynamically evaluate each intervention, taking multiple factors into account. The current study has implications for the development of interventions against norm violations.

Studies have shown that the uncertainty of methane emission estimates over India is as high as 40-60%, largely due to the lack of observations. In India, measurements are limited to a few locations, with the majority of them being flask measurement stations. The observational constraint of the measurements could be greatly improved with the development of a network of continuous measurement stations at well-chosen locations. For this study, we have designed an atmospheric methane measurement network for India using transport modeling techniques and a scaling-factor-based inversion approach. A network optimization algorithm selects the combination of observation locations that gives the most uncertainty reduction in the estimates of posterior methane emission fluxes over India. The backbone of this study is a simple analytical inversion setup that utilizes the STILT (Stochastic Time Inverted Lagrangian Transport) model, a sectorial emission model based on EDGAR, as well as fluxes from wetlands and biomass burning. The state space of the inversion consists of monthly emissions, separated by sector, aggregated spatially to the level of political states.The challenge in network design is to formulate an appropriate target quantity, which the network will be optimized to constrain. Using the annual total emissions as the single target results in a network that will optimally constrain the largest sources, irrespective of their spatial location or the seasonality of the source. Thus, we also included other targets, such as political-state-level emissions, sectoral emissions, and seasonality. For the study, we used a base network of existing stations (“base”) and added further stations from a candidate set (“extended”) on the basis of the incremental uncertainty reduction they provide. We found that more measurement stations along the Indo-Gangetic Plains and North-Eastern India are required. An optimized network was also designed from scratch using the same strategy and it was found to yield similar uncertainty reduction compared to the “base” + “extended” network despite having fewer stations. The effectiveness of the optimal network and the base network in reducing the uncertainties of the different emission categories is assessed and discussed.

CO2Image is a satellite demonstration mission, now in phase B, to be launched by the German Aerospace Center (DLR ) in 2026. The satellite will carry a next generation imaging spectrometer for measuring atmospheric column concentrations of carbon dioxide (CO2). The instrument concept reconciles compact design with fine ground resolution (50-100 m) with decent spectral resolution (1.0-1.3 nm) in the shortwave infrared spectral range (2,000 nm). Thus, CO2Image will enable quantification of point sources with CO2 emission rates of less than 1 MtCO2/a. This will complement global monitoring missions such as CO2M, which are less sensitive to point sources due to their coarser ground resolution, and hyperspectral imagers, which suffer from spectroscopic interference errors that limit the quantification of small sources due to their coarser spectral resolution. Further, CO2Image is sufficiently compact to envision, after successful demonstration, a fleet of sensors operated by public bodies to support and evaluate greenhouse gas emission reduction strategies on community-scales.Here, we will focus on the design choices and performance analyses carried out for building the mission. We have developed an end-to-end simulator that starts out with realistic atmospheric CO2 concentration fields simulated by large-eddy-simulations of CO2 plumes emitted by various point-sources in a 50x50 km2 tile. The latter represents a typical individual target which CO2Image will sample in less than 100 ms in a pushbroom configuration using forward motion compensation. The simulated concentration fields will be used to create synthetic measurements from instrument parameters. These measurements will then be fed into our retrieval and we will use mass balance methods to estimate the compatible emission rates. We evaluate various performance parameters and hypothetical error sources such as calibration errors in terms of their impact on the emission estimates.

The analysis of long, high-quality time series of atmospheric greenhouse gas measurements helps to quantify their seasonal to interannual variations and impact on global climate. These discrete measurement records contain, however, gaps and at times noisy data, influenced by local fluxes or synoptic scale events, hence appropriate filtering and curve-fitting techniques are often used to smooth and gap-fill the atmospheric time series. Previous studies have shown that there is an inherent uncertainty associated with curve-fitting processes which introduces biases based on the choice of mathematical method used for data processing and can lead to scientific misinterpretation of the signal. Further the uncertainties in curve-fitting can be propagated onto the metrics estimated from the fitted curve that could significantly influence the quantification of the metrics and their interpretations. In this context we present a novel-methodology for constraining the uncertainty arising from fitting a smooth curve to the CO2 dry air mole fraction time-series, and propagate this uncertainty onto commonly used metrics to study the seasonal cycle of CO2. We generate an ensemble of fifitted curves from the data using residual bootstrap sampling with loess-fitted residuals, that is representative of the inherent uncertainty in applying the curve-fitting method to the discrete data. The spread of the selected CO2 seasonal cycle metrics across bootstrap time-series provides an estimate of the inherent uncertainty in curve fitting to the discrete data. Further we show that the approach can be extended to other curve-fitting methods by generating multiple bootstrap samples by resampling residuals obtained from processing the data using the widely used CCGCRV filtering method by the atmospheric greenhouse gas measurement community.

Past research has demonstrated that both consequentialist motives (such as deterrence) and deontological motives (such as ‘just deserts’) underlie children’s and adults’ punitive behavior. But what motives do we ascribe to others who pursue punishment? The present work explores this question by assessing which punitive motives children (6- and 7-year-olds, n = 100; 67% white; 55% female) and adults (n = 100; 76% white; 35% female) attribute to individuals who witnessed and punished a transgression (third-party punishment). Beyond this, we varied the social role of the punisher (a teacher, an adult visiting a school, a fellow peer) to examine whether motivational ascriptions vary depending on the social context. Across these contexts, children endorsed a variety of punishment motives but consistently rejected the notion that individuals punish for the purpose of inflicting suffering. Adults—like children—prioritized consequentialist motives but, in more personal contexts (involving a child punishing their peer), considered ‘just deserts’ a more plausible motive. These findings speak to developmental and contextual variation in individuals’ theories about punitive motives and provide insight into how individuals understand and respond to punishment in everyday life.

Adults punish moral transgressions to satisfy both retributive motives (such as wanting antisocial others to receive their ‘just deserts’) and consequentialist motives (such as teaching transgressors that their behaviour is inappropriate). Here, we investigated whether retributive and consequentialist motives for punishment are present in children approximately between the ages of five and seven. In two preregistered studies (N = 251), children were given the opportunity to punish a transgressor at a cost to themselves. Punishment either exclusively satisfied retributive motives by only inflicting harm on the transgressor, or additionally satisfied consequentialist motives by teaching the transgressor a lesson. We found that children punished when doing so satisfied only retributive motives, and punished considerably more when doing so also satisfied consequentialist motives. Together, these findings provide evidence for the presence of both retributive and consequentialist motives in young children. Individuals are willing to punish antisocial others even at a personal cost. Marshall et al. examine the motivational basis of this behaviour from a developmental standpoint, showing that children—like adults—punish others for both retributive and consequentialist reasons.

Forecasting atmospheric CO2 concentrations on synoptic timescales (∼ days) can benefit the planning of field campaigns by better predicting the location of important gradients. One aspect of this, accurately predicting the day-to-day variation in biospheric fluxes, poses a major challenge. This study aims to investigate the feasibility of using a diagnostic light-use-efficiency model, the Vegetation Photosynthesis Respiration Model (VPRM), to forecast biospheric CO2 fluxes on the timescale of a few days. As input, the VPRM model requires downward shortwave radiation, 2 m temperature, and enhanced vegetation index (EVI) and land surface water index (LSWI), both of which are calculated from MODIS reflectance measurements. Flux forecasts were performed by extrapolating the model input into the future, i.e., using downward shortwave radiation and temperature from a numerical weather prediction (NWP) model, as well as extrapolating the MODIS indices to calculate future biospheric CO2 fluxes with VPRM. A hindcast for biospheric CO2 fluxes in Europe in 2014 has been done and compared to eddy covariance flux measurements to assess the uncertainty from different aspects of the forecasting system. In total the range-normalized mean absolute error (normalized) of the 5 d flux forecast at daily timescales is 7.1 %, while the error for the model itself is 15.9 %. The largest forecast error source comes from the meteorological data, in which error from shortwave radiation contributes slightly more than the error from air temperature. The error contribution from all error sources is similar at each flux observation site and is not significantly dependent on vegetation type.

High-quality atmospheric CO2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO2. In this study, we present the first 6 years (2014–2019) of continuous, high-precision measurements of atmospheric CO2 at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO2 regional signal (urn:x-wiley:13541013:media:gcb15905:gcb15905-math-1802) that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter-annual scales, we find differences in phase between urn:x-wiley:13541013:media:gcb15905:gcb15905-math-0001 and the local eddy covariance net ecosystem exchange (EC-NEE), which is interpreted as an indicator of a decoupling between local and non-local drivers of urn:x-wiley:13541013:media:gcb15905:gcb15905-math-0002. In addition, we present how the 2015–2016 El Niño-induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter-annual variability of urn:x-wiley:13541013:media:gcb15905:gcb15905-math-0004 together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO2 exchange. We use both non-optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data-driven non-optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.

Research investigating the early emergence of racial prejudice has been largely limited to contexts in which racial prejudice is most likely to emerge-multiracial societies that have pronounced racial inequality (e.g., United States, South Africa). The present study assessed whether pro-White racial bias is also early emerging in a homogenous Black community that has little exposure to modern media and where children presumably experience less overt discrimination than in past samples. Black African children (N = 214) between 5- and 12-years-old living in rural Uganda exhibited substantial pro-White racial bias, preferring White over Black children 78% of the time. Ugandan children also judged White children as higher status than Black children, and these status judgments predicted their degree of pro-White bias. Our results indicate that pro-White racial biases can emerge even in a homogenous Black community and that, in some contexts, minimal status cues can be sufficient for the early development of racial prejudice.

Atmospheric transport models are often used to simulate the distribution of Greenhouse Gases (GHGs) for atmospheric inverse modeling. However, errors in simulated transport are often neglected in the context of inverse flux estimation. We coupled the commonly used Weather Research and Forecasting (WRF) model with the greenhouse gas module (WRF-GHG), to enable passive tracer transport simulation of CO2 and CH4. As a mesoscale numerical weather prediction model, WRF’s transport is only constrained by global meteorological fields via initialization and at the lateral boundaries; over time the winds in the center of the domain can deviate considerably from these (re-)analysis fields that are constrained by observations. The aim of this study is to have the WRF-simulated transport represent reality as closely as possible, which in this case means staying consistent with the (ERA5) reanalysis fields used as boundary conditions. Therefore, two ways of blending ERA5 with WRF-GHG were tested: (a) regularly restarting the model with fresh initial conditions from ERA5, and (b) nudging the atmospheric winds, temperatures, and moisture to those from ERA5 continuously, using the built-in FDDA option (four-dimensional data assimilation). FDDA constantly forces the model towards the physical reference state (ERA5) by adding an additional tendency term at each grid point and time step. Meteorological variables, as well as the concentrations of CO2 and CH4, were analyzed by comparing with observations. We also compared mixed layer heights (PBLH) with radiosonde-derived observation. We found that performance in horizontal winds and PBLH are slightly better in the nudged simulation (NS) compared to the simulation incorporating frequent restarts (RS). The advantage of grid-nudging is notable when comparing CH4 with aircraft measurements from the CoMet campaign. However, differences in soil moisture increase over time, as soil moisture is not used for nudging. The consequence is a change in the Bowen ratio and thus in vertical mixing, impacting the distribution of GHG tracers in general. To preserve the benefits of nudging and avoid the divergence of soil moisture, we recommend a hybrid approach, combining nudging with daily re-initializations. This technique will be used in an ensemble-based regional inversion system currently under development to make use of satellite-based measurements of GHGs.

High-quality atmospheric CO

Though they cover less than 3 % of the global land area, urban areas are responsible for over 70 % of the global greenhouse gas (GHG) emissions and contain 55 % of the global population. A quantitative tracking of GHG emissions in urban areas is therefore of great importance, with the aim of accurately assessing the amount of emissions and identifying the emission sources. The Weather Research and Forecasting model (WRF) coupled with GHG modules (WRF-GHG) developed for mesoscale atmospheric GHG transport can predict column-averaged abundances of CO2 and CH4 (XCO2 and XCH4). In this study, we use WRF-GHG to model the Berlin area at a high spatial resolution of 1 km. The simulated wind and concentration fields were compared with the measurements from a campaign performed around Berlin in 2014 (Hase et al., 2015). The measured and simulated wind fields mostly demonstrate good agreement. The simulated XCO2 shows quite similar trends with the measurement but with approximately 1 ppm bias, while a bias in the simulated XCH4 of around 2.7 % is found. The bias could potentially be the result of relatively high background concentrations, the errors at the tropopause height, etc. We find that an analysis using differential column methodology (DCM) works well for the XCH4 comparison, as corresponding background biases are then canceled out. From the tracer analysis, we find that the enhancement of XCH4 is highly dependent on human activities. The XCO2 enhancement in the vicinity of Berlin is dominated by anthropogenic behavior rather than biogenic activities. We conclude that DCM is an effective method for comparing models to observations independently of biases caused, e.g., by initial conditions. It allows us to use our high-resolution WRF-GHG model to detect and understand major sources of GHG emissions in urban areas.

Previous research has demonstrated that toddlers are willing to punish those who harm others. This work, however, has predominantly focused on punishment in the form of resource reduction—taking away a resource or withholding access to a resource from an antisocial other. Here, in two studies, we examined whether 4- to 7-year-old children (N = 141) engage in direct, corporal punishment against antisocial others in third-party contexts. Children were given the opportunity to press buttons so that antisocial and prosocial puppets would be hit with a hammer. In Study 1, younger children (∼4-year-olds) hit the antisocial and prosocial puppets indiscriminately, whereas older children (∼7-year-olds) tended to preferentially hit the antisocial puppet. In Study 2, we tested a larger sample of 4- to 7-year-olds, and found that none of the children engaged in corporal punishment. Collapsing across both Studies 1 and 2 also indicated a null effect—children did not engage in third-party corporal punishment. We observed these findings even though children evaluated the antisocial puppet as mean and understood that pressing the hit button hurt the puppets. These findings suggest that children lack a strong desire to corporally punish third-party social wrongdoers. Our results illustrate the importance of considering different types of punishment in assessing the development of third-party punishment, and raise questions about the development of corporal third-party punishment.

BACKGROUND: Breast cancer-related lymphedema (BCRL) is a source of postoperative morbidity for breast cancer survivors. Lymphatic microsurgical preventive healing approach (LYMPHA) is a technique used to prevent BCRL at the time of axillary lymph node dissection (ALND). We report the 5-year experience of a breast surgeon trained in LYMPHA and investigate the outcomes of patients who underwent LYMPHA following ALND for the treatment of cT1-4N1-3M0 breast cancer. METHODS: A retrospective review of patients with T1-4N1-3M0 breast cancer was performed in patients who underwent ALND with and without LYMPHA. Diagnosis of BCRL was made by certified lymphedema therapists. Descriptive statistics and lymphedema surveillance data were analyzed using results of Fisher’s exact or Wilcoxon rank sum tests. Logistic regression and propensity matching were performed to assess the reduction of BCRL occurrence following LYMPHA. RESULTS: In a 5-year period, 132 patients met inclusion criteria with 76 patients undergoing LYMPHA at the time of ALND and 56 patients undergoing ALND alone. Patients who underwent LYMPHA at the time of ALND were significantly less likely to develop BCRL than those who underwent ALND alone (p=0.045). Risk factors associated with BCRL development were increased patient age (p=0.007), BMI (p=0.003), and, in patients undergoing LYMPHA, the number of positive nodes (p=0.026). CONCLUSIONS: LYMPHA may be successfully employed by breast surgeons trained in lymphatic-venous anastomosis at the time of ALND. While research efforts should continue to focus on the prevention and surveillance of BCRL, LYMPHA remains an option to reduce BCRL and improve patient quality of life.

Several ambient air quality records corroborate the severe and persistent degradation of air quality over northern India during the winter months, with evidence of a continued, increasing trend of pollution across the Indo-Gangetic Plain (IGP) over the past decade. A combination of atmospheric dynamics and uncertain emissions, including the post-monsoon agricultural stubble burning, make it challenging to resolve the role of each individual factor. Here we demonstrate the potential use of an atmospheric transport model, the Weather Research and Forecasting model coupled with chemistry (WRF–Chem) to identify and quantify the role of transport mechanisms and emissions on the occurrence of the pollution events. The investigation is based on the use of carbon monoxide (CO) observations from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite and the surface measurement network, as well as the WRF–Chem simulations, to investigate the factors contributing to CO enhancement over India during November 2018. We show that the simulated column-averaged dry air mole fraction (XCO) is largely consistent with TROPOMI observations, with a spatial correlation coefficient of 0.87. The surface-level CO concentrations show larger sensitivities to boundary layer dynamics, wind speed, and diverging source regions, leading to a complex concentration pattern and reducing the observation-model agreement with a correlation coefficient ranging from 0.41 to 0.60 for measurement locations across the IGP. We find that daily satellite observations can provide a first-order inference of the CO transport pathways during the enhanced burning period, and this transport pattern is reproduced well in the model. By using the observations and employing the model at a comparable resolution, we confirm the significant role of atmospheric dynamics and residential, industrial, and commercial emissions in the production of the exorbitant level of air pollutants in northern India. We find that biomass burning plays only a minimal role in both column and surface enhancements of CO, except for the state of Punjab during the high pollution episodes. While the model reproduces observations reasonably well, a better understanding of the factors controlling the model uncertainties is essential for relating the observed concentrations to the underlying emissions. Overall, our study emphasizes the importance of undertaking rigorous policy measures, mainly focusing on reducing residential, commercial, and industrial emissions in addition to actions already underway in the agricultural sectors.

Carbon dioxide (CO2) is an important greenhouse gas, and it accounts for about 20% of the present-day anthropogenic greenhouse effect. Atmospheric CO2 is cycled between the terrestrial biosphere and the atmosphere through various land-surface processes and thus links the atmosphere and terrestrial biosphere through positive and negative feedback. Since multiple trace gas elements are linked by common biogeochemical processes, multi-species analysis is useful for reinforcing our understanding and can help in partitioning CO2 fluxes. For example, in the northern hemisphere, CO2 has a distinct seasonal cycle mainly regulated by plant photosynthesis and respiration and it has a distinct negative correlation with the seasonal cycle of the δ13C isotope of CO2, due to a stronger isotopic fractionation associated with terrestrial photosynthesis. Therefore, multi-species flask-data measurements are useful for the long-term analysis of various green-house gases. Here we try to infer the complex interaction between the atmosphere and the terrestrial biosphere by multi-species analysis using atmospheric flask measurement data from different NOAA flask measurement sites across the northern hemisphere.This study focuses on the long-term changes in the seasonal cycle of CO2 over the northern hemisphere and tries to attribute the observed changes to driving land-surface processes through a combined analysis of the δ13C seasonal cycle. For this we generate metrics of different parameters of the CO2 and δ13C seasonal cycle like the seasonal cycle amplitude given by the peak-to-peak difference of the cycle (indicative of the amount of CO2 taken up by terrestrial uptake), the intensity of plant productivity inferred from the slope of the seasonal cycle during the growing season , length of growing season and the start of the growing season. We analyze the inter-relation between these metrics and how they change across latitude and over time. We hypothesize that the CO2 seasonal cycle amplitude is controlled both by the intensity of plant productivity and period of the active growing season and that the timing of the growing season can affect the intensity of plant productivity. We then quantify these relationships, including their variation over time and latitudes and describe the effects of an earlier start of the growing season on the intensity of plant productivity and the CO2 uptake by plants.

During the COVID-19 pandemic lockdowns, human activities are strongly restricted, which results in a reduction in greenhouse gas (GHG) emissions associated with changes in energy consumptions. The Copernicus Atmosphere Monitoring Service (CAMS) reported a 10.3% decrease in CO2 fossil fuel emissions during the first lockdown (February-July, 2020) of the COVID-19 pandemic throughout Europe. Using our WRF modeling framework built for the Munich area [1,3] and the column measurements from our automated Munich Urban Carbon Column network (MUCCnet, [2]), we aim to quantify the reduction of GHG emissions within Munich during the COVID-19 pandemic.Our high-resolution modeling framework can simulate the sources, sinks, and emissions of CO2 and CH4 at a spatial resolution of up to 400m. The initial and boundary conditions for meteorological fields are taken from ERA5 and CAMS data is used for initializing the initial and lateral tracer boundary conditions. Anthropogenic emissions below ~1 km altitude above the ground level are obtained from TNO-GHGco v1.1 at a resolution of 1 km2. Various tagged tracers are included to quantify the contribution from different emission categories (such as biogenic emissions from wetlands, emissions from road transport, industry, etc). By refining the vegetation classification using the Dynamic Land Cover map of the Copernicus Global Land Service at 100 m resolution (CGLS-LC100), the urban biogenic signals of CO2 can be well captured using the diagnostic light-use-efficiency biosphere model VPRM (Vegetation Photosynthesis and Respiration Model), which is driven by MODIS indices. Moreover, we integrate urban canopy information derived from World Urban Database and Access Portal Tools (WUDAPT) classified by local climate zones (LCZs) [4] into our model infrastructure. Incorporating precise urban land use data in WRF helps to capture more urban transport features, improving the model behavior within urban areas.We targeted the pandemic period from February to July 2020 and the same period in 2019 to make a comparison. Thanks to our nearly continuous column measurements during the COVID-19 pandemic, we are able to evaluate our simulated GHG concentrations by comparing them to the measurement results. Furthermore, an estimation of GHG emissions reduction in Munich during the targeted period will be obtained by performing a Bayesian inversion approach incorporating the simulated concentration enhancements from tagged tracers in WRF.[1] Zhao, X., Chen, J., Marshall, J., Galkowski, M., Gerbig, C., Hachinger, S., Dietrich, F., Lan, L., Knote, C., and van der Gon, H.D., 2020. A semi-operational near-real-time Modelling Infrastructure for assessing GHG emissions in Munich using WRF-GHG. In EGU General Assembly 2020.[2] Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: Munich permanent urban greenhouse gas column observing network, Atmos. Meas. Tech. Discuss. https://doi.org/10.5194/amt-2020-300, in review, 2020.[3] Zhao, X., Marshall, J., Hachinger, S., Gerbig, C., Frey, M., Hase, F., and Chen, J.: Analysis of total column CO2 and CH4 measurements in Berlin with WRF-GHG, Atmos. Chem. Phys., 19, 11279–11302, https://doi.org/10.5194/acp-19-11279-2019, 2019.[4] Demuzere, M., Bechtel, B., Middel, A., & Mills, G. (2019). Mapping Europe into local climate zones. PLOS ONE, 14(4), e0214474. https://doi.org/10.1371/journal.pone.0214474.

Climate change mitigation strategies require regional-scale estimations of CO2 fluxes, but estimates are often tampered with a significant amount of uncertainties due to multiple factors. In the case of the top-down approach, the error associated with the forward transport model is one of the major causes of uncertainty. Current generation global atmospheric transport models are more often simulated at a horizontal resolution of one degree or less, which omits a large number of small-scale processes and creates a significant amount of uncertainty in estimations. Attempts for the estimation of CO2 fluxes at fine scales over India using the top-down approach is essentially new compared to some other parts of the globe like North America or Europe. The study focuses on implementing an inverse modelling system, by considering high-resolution atmospheric transport and flux distribution, to retrieve the fluxes at spatial scales relevant for ecosystem and policy-making. Using WRF-Chem (GHG) model, we estimate the transport error in CO2 simulations over India during July and November 2017 which is associated with different processes whose scale is smaller than the resolution of current-generation global transport models. We show that the overall sub-grid variability (or representation error) at the surface can go up to ~10 ppm for the surface CO2, which is markedly higher than the sampling errors. Total column-averaged CO2 also shows similar variability in the spatial pattern though the magnitude is less compared to the surface, which indicates the prominence of heterogeneity at surface flux in modulating the entire column variability. Our results show that there exist regional differences in sub-grid scale variability for both surface and column CO2 with very high magnitude observed at coastal and mountain regions and at emission hot spots. In addition to spatial variability, the sub-grid variability of CO2 over India exhibits seasonal variations as well. The vertical distribution of sub-grid variability during July shows its association with the monsoon circulations, which is much different than those during November. Our estimates suggest that the terrain heterogeneity alone can explain about 53-63% of the surface representation errors over the domain, which shows the importance of using accurate Digital Elevation Models in the atmospheric transport model simulations. With underlying assumptions, the total flux uncertainty solely due to the unresolved sub-grid scale variations is estimated to be 8.1 to 14.4% of the total NEE. These results will be discussed and presented in the context of our attempts towards estimating the source-sink distribution of CO2 over India.

This study investigates the use of total column methane measurements from the TROPOMI satellite instrument for estimating the global sources and sinks of methane. A bias correction method has been developed based on a comparison between the satellite measurements and an inversion using surface measurements only, building on the experience using GOSAT data. The bias correction is applied to the satellite measurements prior to the use of the data in the inversion. Results will be shown of inversions using the TM5 4D-VAR and CarboScope inverse modelling systems applied to two years of TROPOMI data. The inversion-optimized methane mixing ratios are inter-compared and validated against independent surface (WMO-GAW), Aircraft (ATom) and total column (TCCON) observations. The derived methane fluxes are aggregated over selected geographic regions, to compare the optimised methane emissions from TM5-4DVAR, CarboScope, and GOSAT inversions from the Copernicus Atmospheric Monitoring Service. Methane surface mixing ratios derived from the TROPOMI inversion show a good agreement with the surface measurements in general. Near areas with high aerosol optical thickness (e.g. the Sahara) we see significant adjustments in the surface fluxes, compensating for model-data differences, pointing to influences of residual uncorrected systematic errors in the data. The total column comparison with TCCON measurements shows a slight North-South bias gradient. These finding are investigated in further detail by comparing results using the operational retrieval product to the use of the scientific RemoTeC and WFMD retrievals. Encouragingly, both the TM5 and CarboScope inversions show similar increments in the aggregated fluxes over time. The seasonal cycle in the posterior fluxes is different from that of the a a priori fluxes, which were the same for both inversion systems.

Methane (CH4) is the second most important anthropogenic greenhouse gas, whose atmospheric concentration is modulated by human-induced activities, and it has a larger global warming potential than carbon dioxide (CO2). Because of its short atmospheric lifetime relative to that of CO2, the reduction of the atmospheric abundance of CH4 is an attractive target for short term climate mitigation strategies. However, reducing the atmospheric CH4 concentration requires a reduction of its emissions and, therefore, knowledge of its sources is essential. For this reason, the CO2 and Methane (CoMet) campaign in early summer of 2018 was initiated with the primary goal of assessing emissions of one of the largest CH4 emission hot spots in Europe, the Upper Silesian Coal Basin (USCB) in southern Poland, using top-down approaches and inventory data. In this campaign, a variety of instruments (both in situ and remote sensing) and platforms (e.g., ground-based and airborne) were deployed, which were supplemented by modeling activities supporting the flight planning and the interpretation of the observations. Consequently, CH4 emissions originating from ~54 coal mine ventilation shafts distributed over an area of around 60 × 40 km2 could be investigated on different scales, ranging from single shafts over smaller clusters up to the entire basin. In this study, we will focus on CH4 column anomalies retrieved from spectral radiance observations, which were acquired by the 1D nadir-looking passive remote sensing Methane Airborne MAPper (MAMAP) instrument, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) method. The column anomalies are combined with wind lidar measurements and inverted to cross-sectional fluxes for different flight tracks making use of a mass balance approach. These fluxes are subsequently used to assess the reported emissions of small clusters of ventilation shafts. The MAMAP CH4 column observations allow for accurate assignment of observed fluxes to small clusters of ventilation shafts. CH4 fluxes are estimated for 4 clusters comprising 23 ventilation shafts in total, which are responsible for about 40 % of the total CH4 emissions from mining in the target area. The observations used were made during multiple overflights on different days between 28 May and 7 June 2018. The final averaged CH4 fluxes for the single clusters (or sub-clusters) range from about 1 to 9 t CH4 hr−1 at the time of the campaign. The range of fluxes observed at one cluster during different overflights can vary by as much as 50 % of the respective averaged value. Associated errors (1-σ) are usually between 15 % and 59 % of the averaged flux, mainly depending on the prevailing wind conditions, the number of flight tracks, and the magnitude of the flux itself. Comparison to known hourly emissions, where available, shows good agreement with the computed fluxes within the uncertainties. In the case that only annually reported emissions are available for comparison with the observations, caution is required due to potential fluctuations of the emissions during one year or even within hours. To measure emissions even more precisely and to further unravel them for allocation to individual shafts in a complex source region as encountered in the USCB, imaging remote sensing instruments are recommended.

The Paris Agreement of the United Nations Framework Convention on Climate Change is a binding international treaty signed by 196 nations to limit their greenhouse gas emissions through ever-reducing Nationally Determined Contributions and a system of 5-yearly Global Stocktakes in an Enhanced Transparency Framework. To support this process, the European Commission initiated the design and development of a new Copernicus service element that will use Earth observations mainly to monitor anthropogenic carbon dioxide (CO2) emissions. The CO2 Human Emissions (CHE) project has been successfully coordinating efforts of its 22 consortium partners, to advance the development of a European CO2 monitoring and verification support (CO2MVS) capacity for anthropogenic CO2 emissions. Several project achievements are presented and discussed here as examples. The CHE project has developed an enhanced capability to produce global, regional and local CO2 simulations, with a focus on the representation of anthropogenic sources. The project has achieved advances towards a CO2 global inversion capability at high resolution to connect atmospheric concentrations to surface emissions. CHE has also demonstrated the use of Earth observations (satellite and ground-based) as well as proxy data for human activity to constrain uncertainties and to enhance the timeliness of CO2 monitoring. High-resolution global simulations (at 9 km) covering the whole of 2015 (labelled CHE nature runs) fed regional and local simulations over Europe (at 5 km and 1 km resolution) and supported the generation of synthetic satellite observations simulating the contribution of a future dedicated Copernicus CO2 Monitoring Mission (CO2M).

The COVID-19 pandemic has necessitated the adoption of a number of policies that aim to reduce the spread of the disease by promoting housing stability. Housing precarity, which includes both the risk of eviction and utility disconnections or shut-offs, reduces a person’s ability to abide by social distancing orders and comply with hygiene recommendations. Our analysis quantifies the impact of these various economic policies on COVID-19 infection and death rates using panel regression techniques to control for a variety of potential confounders. We find that policies that limit evictions are found to reduce COVID-19 infections by 3.8% and reduce deaths by 11%. Moratoria on utility disconnections reduce COVID-19 infections by 4.4% and mortality rates by 7.4%. Had such policies been in place across all counties (i.e., adopted as federal policy) from early March 2020 through the end of November 2020, our estimated counterfactuals show that policies that limit evictions could have reduced COVID-19 infections by 14.2% and deaths by 40.7%. For moratoria on utility disconnections, COVID-19 infections rates could have been reduced by 8.7% and deaths by 14.8%. Housing precarity policies that prevent eviction and utility disconnections have been effective mechanisms for decreasing both COVID-19 infections and deaths.