Large Bias Research Articles

Optimized Approach for Measuring Ethylene Oxide in Mobile Source Exhaust.

There is a growing awareness of the health impacts of ethylene oxide (EtO) and its role as a carcinogenic and mutagenic air contaminant of concern. Given the need to better understand EtO emissions and associated health effects, it is imperative to overcome the significant challenges associated with EtO measurement in complex air matrices, such as combustion emissions. This work focused on addressing these challenges by evaluating the utility of widely used canister-based EtO ambient measurement approaches, EPA Methods TO-15 and TO-15A, to investigate the presence of EtO in heavy-duty diesel vehicle (HDDV) exhaust. Chassis dynamometer testing was performed on two HDDVs and emissions samples were collected and analyzed following TO-15/TO-15A. Initial testing utilizing TO-15 led to the identification of a diesel exhaust constituent, ethyl nitrite, that coeluted with EtO during analysis and contributed large positive bias. An optimized TO-15A analytical approach was developed and utilized to measure EtO in diesel exhaust from two HDDVs in additional dynamometer tests. Using this optimized approach, EtO was not detected in HDDV exhaust in these tests. This work highlights the importance of utilizing this optimized approach to accurately quantify EtO in mobile source exhaust and may also be needed for testing other combustion sources.

Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling

Abstract. Ground-level ozone (O3) pollution is a persistent environmental concern, even in regions that have made efforts to reduce emissions. This study focuses on the state of Arizona, which has experienced elevated O3 concentrations over past decades and contains two non-attainment areas as designated by the U.S. Environmental Protection Agency. Using the Weather Research and Forecasting with Chemistry (WRF-Chem) model, we examine O3 levels in the semi-arid and arid regions of Arizona. Our analysis focuses on the month of June between 2017 and 2021, a period characterized by high O3 levels before the onset of the North American Monsoon (NAM). Our evaluation of the WRF-Chem model against surface Air Quality System (AQS) observations reveals that the model adeptly captures the diurnal variation of hourly O3 levels and the episodes of O3 exceedance through the maximum daily 8 h average (MDA8) O3 concentrations. However, the model tends to overestimate surface NO2 concentrations, particularly during nighttime hours. Among the three cities studied, Phoenix (PHX) and Tucson (TUS) exhibit a negative bias in both hourly and MDA8 O3 levels, while Yuma demonstrates a relatively large positive bias. The simulated mean hourly and MDA8 O3 concentrations in Phoenix are 44.6 and 64.7 parts per billion (ppb), respectively, compared to observed values of 47.5 and 65.7 ppb, resulting in mean negative biases of −2.9 and −1.0 ppb, respectively. Furthermore, the analysis of the simulated ratio of formaldehyde (HCHO) to NO2 (HCHO/NO2; FNR), reveals interesting insights of the sensitivity of O3 to its precursors. In Phoenix, the FNR varies from a VOC (volatile organic compound)-limited regime in the most populated areas to a transition between VOC-limited and NOx-limited regimes throughout the metro area, with an average FNR of 1.15. In conclusion, this study sheds light on the persistent challenge of ground-level O3 pollution in semi-arid and arid regions, using the state of Arizona as a case study.

Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)

Abstract. During the last few decades, the impact of multiphase chemistry on secondary organic aerosols (SOAs) has been demonstrated to be the key to explaining laboratory experiments and field measurements. However, global atmospheric models still show large biases when simulating atmospheric observations of organic aerosols (OAs). Major reasons for the model errors are the use of simplified chemistry schemes of the gas-phase oxidation of vapours and the parameterization of heterogeneous surface reactions. The photochemical oxidation of anthropogenic and biogenic volatile organic compounds (VOCs) leads to products that either produce new SOA or are taken up by existing aqueous media like cloud droplets and deliquescent aerosols. After partitioning, aqueous-phase processing results in polyols, organosulfates, and other products with a high molar mass and oxygen content. In this work, we introduce the formation of new low-volatility organic compounds (LVOCs) to the multiphase chemistry box model CAABA/MECCA. Most notable are the additions of the SOA precursors, limonene and n-alkanes (5 to 8 C atoms), and a semi-explicit chemical mechanism for the formation of LVOCs from isoprene oxidation in the gas and aqueous phases. Moreover, Henry's law solubility constants and their temperature dependences are estimated for the partitioning of organic molecules to the aqueous phase. Box model simulations indicate that the new chemical scheme predicts the enhanced formation of LVOCs, which are known for being precursor species to SOAs. As expected, the model predicts that LVOCs are positively correlated to temperature but negatively correlated to NOx levels. However, the aqueous-phase processing of isoprene epoxydiols (IEPOX) displays a more complex dependence on these two key variables. Semi-quantitative comparison with observations from the SOAS campaign suggests that the model may overestimate methylbutane-1,2,3,4-tetrol (MeBuTETROL) from IEPOX. Further application of the mechanism in the modelling of two chamber experiments, one in which limonene is consumed by ozone and one in which isoprene is consumed by NO3 shows a sufficient agreement with experimental results within model limitations. The extensions in CAABA/MECCA are transferred to the 3D atmospheric model MESSy for a comprehensive evaluation of the impact of aqueous- and/or aerosol-phase chemistry on SOA at a global scale in a follow-up study.

Models of Inter-Moult Period for Antarctic Krill - Lack of Progress and Promulgation of Unreliable Models Calibrated Using Indirect Observations

Aims: Two methods of calibrating regression models of inter-moult period (IMP) as a function of temperature exposure (T) for crustaceans, in particular, Antarctic krill (Euphausia superba) are reviewed in terms of both theoretical and empirical properties in order to make recommendations on the application of the methods and/or the use of the resultant fitted models. Methodology: The method and fitted model that used a meta-analysis of published results from laboratory-reared krill using means of directly observed IMP for a range of controlled, constant temperature regimes has valid theoretical and empirical support. The alternative used moult frequencies obtained as a “byproduct” of 5-d Instantaneous Growth Rate (IGR) experiments carried out at sea for which individual IMPs were not directly observed. Instead mean IMP given T and animal total length (L) was predicted using the moult frequencies disaggregated to binary data of moulted versus not-moulted as dependent variable in the calibration of a logistic regression on T and L. The shape of the daily development rate, R, the inverse of IMP, versus T response curve fitted using direct observations is a classical monotonically increasing curve whereas for combinations of sex/maturity classes the curves fitted using indirect observations are parabola-like with sexually dimorphic concavities of either up or down. Four sources of bias in predictions of mean IMP using the indirect observations and estimation method are described. One source due to an unrepresentative sampling frame can lead to large positive bias in estimated mean IMP based on theory which has been absent until now and that applies a discrete uniform distribution for next moult date corresponding to ideal asynchrony. This bias and that due to IGR experimental measurement error in T cannot be remedied. Conclusion: The indirect method and the corresponding fitted models are unreliable and should not be used.

Applying oversampling before cross-validation will lead to high bias in radiomics

Class imbalance is often unavoidable for radiomic data collected from clinical routine. It can create problems during classifier training since the majority class could dominate the minority class. Consequently, resampling methods like oversampling or undersampling are applied to the data to class-balance the data. However, the resampling must not be applied upfront to all data because it would lead to data leakage and, therefore, to erroneous results. This study aims to measure the extent of this bias. Five-fold cross-validation with 30 repeats was performed using a set of 15 radiomic datasets to train predictive models. The training involved two scenarios: first, the models were trained correctly by applying the resampling methods during the cross-validation. Second, the models were trained incorrectly by performing the resampling on all the data before cross-validation. The bias was defined empirically as the difference between the best-performing models in both scenarios in terms of area under the receiver operating characteristic curve (AUC), sensitivity, specificity, balanced accuracy, and the Brier score. In addition, a simulation study was performed on a randomly generated dataset for verification. The results demonstrated that incorrectly applying the oversampling methods to all data resulted in a large positive bias (up to 0.34 in AUC, 0.33 in sensitivity, 0.31 in specificity, and 0.37 in balanced accuracy). The bias depended on the data balance, and approximately an increase of 0.10 in the AUC was observed for each increase in imbalance. The models also showed a bias in calibration measured using the Brier score, which differed by up to −0.18 between the correctly and incorrectly trained models. The undersampling methods were not affected significantly by bias. These results emphasize that any resampling method should be applied correctly only to the training data to avoid data leakage and, subsequently, biased model performance and calibration.

Magnetic field dependent elastic moduli and internal frictions in nickel alloy revealed by a quantitative electromechanical impedance method

Accurately and quickly determining the moduli and damping properties of ferromagnetic materials with bias magnetic fields is very important for vibration control and instrument design. In this work, the magnetoelastic properties of a nickel alloy were investigated by using a quantitative electromechanical impedance (Q-EMI) method under bias magnetic fields from 0 to 1364 Oe. Measurement results demonstrate that both the Young’s modulus and shear modulus of the nickel increase steadily with the bias magnetic field, tending to saturate under high fields. The internal friction of nickel initially increases and subsequently decrease with the magnetic field, exhibiting a pronounced peak. Meanwhile, it is found that the torsional internal friction is always considerably larger than the longitudinal internal friction. The amplitude dependent internal friction (ADIF) of the nickel alloy under bias magnetic fields was further studied using a dog-bone shaped specimen and a strain-amplifier horn. Results show that the specimen under zero field shows the most significant ADIF when the strain amplitude exceeds 2 ⅹ 10−5. While under a large bias field over 1000 Oe, the ADIF cannot appear until the strain amplitude is over 1 ⅹ 10−4 strain, indicating that the ferromagnetic domains had been stabilized by the large bias field. This study demonstrates the superiority of the Q-EMI method in probing the magnetomechanical properties of ferromagnetic materials.

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies

BackgroundA Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs.MethodsWe compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed.ResultsFor most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate.ConclusionsWe recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.

Adaptation of state-of-the-art neural network architectures to interference fringe reduction in absorption spectroscopy

State-of-the-art neural network architectures in image classification and natural language processing were applied to interference fringe reduction in absorption spectroscopy by interpreting the data structure accordingly. A model was designed for temporal interpolation of background spectra and a different model was created for gas concentration fitting. The networks were trained on experimental data provided by a wavelength modulation spectroscopy instrument and the best performing architectures were analyzed further to evaluate generalization performance, robustness and transferability. A BERT-styled fitter achieved the best performance on the validation set and reduced the mean squared error of fitted amplitude by 99.5%. However, analysis of the de-noising behavior showed large biases. A U-Net styled convolutional neural network reduced the mean squared error of the interpolation by 93.2%. Evaluation on a test set provided evidence that the combination of model interpolation and linear fitting was robust and the detection limit was improved by 52.4%. Transferring the trained interpolator model to a different spectrometer setup showed no chaotic out-of-distribution effects. Additional fine-tuning further increased the performance. Neural network architectures cannot be generally applied to all absorption spectroscopy tasks. However, given the right task and the data representation, robust performance increase is achievable.

Long-range order enabled stability in quantum dot light-emitting diodes.

Light-emitting diodes (LEDs) based on perovskite quantum dots (QDs) have produced external quantum efficiencies (EQEs) of more than 25% with narrowband emission1,2, but these LEDs have limited operating lifetimes. We posit that poor long-range ordering in perovskite QD films-variations in dot size, surface ligand density and dot-to-dot stacking-inhibits carrier injection, resulting in inferior operating stability because of the large bias required to produce emission in these LEDs. Here we report a chemical treatment to improve the long-range order of perovskite QD films: the diffraction intensity from the repeating QD units increases three-fold compared with that of controls. We achieve this using a synergistic dual-ligand approach: an iodide-rich agent (aniline hydroiodide) for anion exchange and a chemically reactive agent (bromotrimethylsilane) that produces a strong acid that in situ dissolves smaller QDs to regulate size and more effectively removes less conductive ligands to enable compact, uniform and defect-free films. These films exhibit high conductivity (4 × 10-4 S m-1), which is 2.5-fold higher than that of the control, and represents the highest conductivity recorded so far among perovskite QDs. The high conductivity ensures efficient charge transportation, enabling red perovskite QD-LEDs that generate a luminance of 1,000 cd m-2 at a record-low voltage of 2.8 V. The EQE at this luminance is more than 20%. Furthermore, the stability of the operating device is 100 times better than previous red perovskite LEDs at EQEs of more than 20%.

Out-of-Equilibrium Fluctuation-Dissipation Bounds.

We prove a general inequality between the charge current and its fluctuations valid for any weakly interacting coherent electronic conductor and for any stationary out-of-equilibrium condition, thereby going beyond established fluctuation-dissipation relations. The developed fluctuation-dissipation bound saturates at large temperature bias and reveals additional insight for heat engines, since it limits the output power by power fluctuations. It is valid when the thermodynamic uncertainty relations break down due to quantum effects and provides stronger constraints close to thermovoltage.

THE AGREEMENT BETWEEN HEART RATE MEASURED USING UNATTENDED AUTOMATED BLOOD PRESSURE MEASUREMENT AND OTHER METHODS OF BLOOD PRESSURE ASSESSMENT

Objective: The trial was aimed to assess the agreement between resting heart rate (HR) measured using unattended automatic (uAOBPM) and office (OBPM) or 24-h ambulatory blood pressure measurement (ABPM). Design and method: A total of 110 participants were included in the study. All underwent measurements using uAOBPM, OBPM, and ABPM. Results of ABPM were analysed in 3 periods: 24h (24h-ABPM), day activity (aABPM) and night rest (nABPM). Continuous variables were presented as means with standard deviation, while categorical variables were expressed as numbers and percentages. The comparisons were conducted using the paired t-test and Bland-Altman statistics. Results: Mean age was 56.5 ± 18.3 years. Mean HR measured using uAOBPM (70.8 ± 12.5 b.p.m.) was lower than measured using OBPM (72.8 ± 12.6 b.p.m., p<0.001), higher than measured using 24h-ABPM (67.5 ± 10.2 b.p.m., p<0.001) or nABPM (62 ± 8.7 b.p.m., p<0.001). There was no difference between HR measured using uAOBPM and aABPM (70.3 ± 11.2 b.p.m., p=0.5). Bland-Altman analysis showed smallest bias when HR was measured using uAOBPM and ABPM 0.4 (95% CI:-0.8 to 1.6) b.p.m. and -2 (95% CI:- 2.8 to -1.3) b.p.m., though the range between lower and upper limits of agreement was higher when uAOBPM has been compared with aABPM and not with OBPM. The largest bias between limits of agreement was found when uAOBPM was compared with nABPM 8.8 (95% CI:7.2 to 10.4) b.p.m. Also the largest range between lower and upper limits of agreement was when agreement between uAOBPM and nABPM was assessed. The results of Bland-Altman analysis is presented using Figure 1. Conclusions: The best agreement between HR measurements was observed when uAOBPM was compared with OBPM or aABPM.

Forecasting High Wind Events in the HRRR Model over Wyoming and Colorado. Part I: Evaluation of Wind Speeds and Gusts

Abstract Strong wind events cause significant societal damage ranging from loss of property and disruption of commerce to loss of life. Over portions of the United States, the strongest winds occur in the cold season and may be driven by interactions with the terrain (downslope winds, gap flow, and mountain wave activity). In the first part of this two-part series, we evaluate the High-Resolution Rapid Refresh (HRRR) model wind speed and gust forecasts for the 2016–22 winter months over Wyoming and Colorado, an area prone to downslope windstorms and gap flows due to its complex topography. The HRRR model exhibits a positive bias for low wind speeds/gusts and a large negative bias for strong wind speeds/gusts. In general, the model misses many strong wind events, but when it does predict strong winds, there is a high false alarm probability. An analysis of proxies for surface winds is conducted. Specifically, 700- and 850-mb (1 mb = 1 hPa) geopotential height gradients are found to be good proxies for strong wind speeds and gusts at two wind-prone locations in Wyoming. Given the good agreement between low-level height gradients and surface wind speeds yet a strong negative bias for strong wind speeds and gusts, there is a potential shortcoming in the boundary layer physics in the HRRR model with regard to predicting strong winds over complex terrain, which is the focus of the second part of this two-part study. Last, the sites with the largest strong wind speed bias are found to mostly sit on the leeward side of high mountains, suggesting that the HRRR model performs poorly in the prediction of downslope windstorms. Significance Statement We investigate the performance of the High-Resolution Rapid Refresh (HRRR) model with respect to strong wintertime wind speeds and gusts over the complex terrain of Wyoming and Colorado. We show that the overall performance of the HRRR model is low with regard to strong wind speed and wind gust forecasts across the investigated winter seasons, with a large negative bias in predicted strong wind speeds and gusts and a small positive bias for weak wind speeds and gusts. The largest biases are found to be on the leeward side of high mountains, indicating poor prediction of downslope winds. This study also utilizes National Weather Service forecasting metrics to understand their performance with respect to strong wind forecasts, and we find that they provide skill in forecasting these events.

Forecasting High Wind Events in the HRRR Model over Wyoming and Colorado. Part II: Sensitivity of Surface Wind Speeds to Model Resolution and Physics

Abstract Strong wind events can cause severe economic loss and societal impacts. Peak winds over Wyoming and Colorado occur during the wintertime months, and in the first part of this two-part series, it was shown that the High-Resolution Rapid Refresh (HRRR) model displays large negative biases with respect to strong wind events. In this part of the study, we address two questions: 1) does increasing the horizontal resolution improve the representation of strong wind events over this region, and 2) are the biases in HRRR-forecasted winds related to the selected planetary boundary layer (PBL), surface layer (SL), and/or land surface model (LSM) parameterizations? We conduct Weather Research and Forecasting (WRF) Model simulations to address these two main questions. Increasing the horizontal resolution leads to a small improvement in the simulation of strong wind speeds over the complex terrain of Wyoming and Colorado. In general, changes in the PBL, SL, and LSM parameterizations show much larger changes in simulated wind speeds compared to increasing the model resolution alone. Specifically, changing from the Mellor–Yamada–Nakanishi–Niino scheme to the Mellor–Yamada–Janjić PBL and SL schemes results in nearly no change in the r2 values, but there is a decrease in the magnitude of the strong wind speed bias (from −12.52 to −10.16 m s−1). We attribute these differences to differences in the diagnosis of surface wind speeds and mixing in the boundary layer. Further analysis is conducted to determine the value of 1-km forecasts of strong winds compared with wind speed diagnostics commonly used by the National Weather Service. Significance Statement Motivated by prior studies showing low skill in the prediction of strong winds using state-of-the-art weather forecast models, in this study, we aim to investigate two questions: 1) does increasing the horizontal resolution improve the prediction of strong wind events over the complex terrain of Wyoming and Colorado, and 2) are the biases in High-Resolution Rapid Refresh (HRRR) forecasted winds related to the selected planetary boundary layer, surface layer, and/or land surface model parameterizations? We find that increasing the horizontal resolution provides a slight improvement in the prediction of strong winds. Further, considerable improvement in the prediction of strong winds is found for varying boundary layer, surface layer, and land surface parameterizations.

Pushing the high count rate limits of scintillation detectors for challenging neutron-capture experiments

One of the critical aspects for the accurate determination of neutron capture cross sections when combining time-of-flight and total energy detector techniques is the characterization and control of systematic uncertainties associated to the measuring devices. In this work we explore the most conspicuous effects associated to harsh count rate conditions: dead-time and pile-up effects. Both effects, when not properly treated, can lead to large systematic uncertainties and bias in the determination of neutron cross sections. In the majority of neutron capture measurements carried out at the CERN n_TOF facility, the detectors of choice are the C6D6 liquid-based either in form of large-volume cells or recently commissioned sTED detector array, consisting of much smaller-volume modules. To account for the aforementioned effects, we introduce a Monte Carlo model for these detectors mimicking harsh count rate conditions similar to those happening at the CERN n_TOF 20 m flight path vertical measuring station. The model parameters are extracted by comparison with the experimental data taken at the same facility during 2022 experimental campaign. We propose a novel methodology to consider both, dead-time and pile-up effects simultaneously for these fast detectors and check the applicability to experimental data from 197Au(n, γ), including the saturated 4.9 eV resonance which is an important component of normalization for neutron cross section measurements.

Accurate measurement of field size is essential for analysis of smallholder survey data

ContextAgronomic surveys play a vital role in identifying on-farm yield constraints and opportunities to increase productivity, input-use efficiency, and farmer profit. These surveys typically rely on farmer-reported data, where crop yield and applied inputs per hectare are estimated based on farmer-reported total harvested output and applied inputs per field and associated field size. At question is how accurate field size is, which is vital in the case of smallholders as small inaccuracies can lead to large biases in estimated yield and inputs. ObjectivesThe goals of this study are to assess current approaches to determine field size in smallholders, analyze biases between measurements and farmer estimates, and their influence on the analysis of the survey data. Cost-effective approaches for accurate field size measurement are also discussed. MethodsWe reviewed published studies that used survey data collected from smallholders to determine the extent to which field size is independently validated by researchers. Subsequently, we used data from thousands of smallholder fields in Southeast Asia to compare farmer-reported versus measured field size and examined the associated impact on estimated yields, inputs, and their relationships. ResultsWe found that most published studies using survey data did not validate field size reported by farmers. Furthermore, our analysis of the field size data from smallholders in Southeast Asia showed substantial differences between farmer-reported and measured field size, resulting in significant biases in estimated yield and applied inputs, and leading to spurious relationships between them. ConclusionUncertainty in field size can mislead analysis of smallholder survey data. ImplicationsFuture studies relying on survey data from smallholders should incorporate measurements of field size for accurate determination of crop yield and inputs.

A pre-whitening with block-bootstrap cross-correlation procedure for temporal alignment of data sampled by eddy covariance systems

The eddy covariance (EC) method is a standard micrometeorological technique for monitoring the exchange rate of the main greenhouse gases across the interface between the atmosphere and ecosystems. One of the first EC data processing steps is the temporal alignment of the raw, high frequency measurements collected by the sonic anemometer and gas analyser. While different methods have been proposed and are currently applied, the application of the EC method to trace gases measurements highlighted the difficulty of a correct time lag detection when the fluxes are small in magnitude. Failure to correctly synchronise the time series entails a systematic error on covariance estimates and can introduce large uncertainties and biases in the calculated fluxes. This work aims at overcoming these issues by introducing a new time lag detection procedure based on the assessment of the cross-correlation function (CCF) between variables subject to (i) a pre-whitening based on autoregressive filters and (ii) a resampling technique based on block-bootstrapping. Combining pre-whitening and block-bootstrapping facilitates the assessment of the CCF, enhancing the accuracy of time lag detection between variables with correlation of low order of magnitude (i.e. lower than -1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-1$$\\end{document}) and allowing for a proper estimate of the associated uncertainty. We expect the proposed procedure to significantly improve the temporal alignment of the EC time-series measured by two physically separate sensors, and to be particularly beneficial in centralised data processing pipelines of research infrastructures (e.g. the Integrated Carbon Observation System, ICOS-RI) where the use of robust and fully data-driven methods, like the one we propose, constitutes an essential prerequisite.

Extension of the gamma concept to handle changing environment during shelf-life. Applied to an example with Listeria monocytogenes in salads with changing pH.

Prediction of microbial growth is the main topic within the area of predictive microbiology. A commonly used framework is the so-called gamma concept which is to view the bacterial growth rate as a product of a bacteria- and medium specific maximum instantaneous factor, and several environmental factors. Traditionally environmental factors are assumed to be constant, leading to exponential growth. In the current study, we expand the gamma concept to include changing dynamic environmental factors, exemplified by pH, and show how this might affect estimates for Listeria monocytogenes growth rates and thereby predicted shelf-life. The study shows how substantial environmental changes might lead to large biases for the estimated growth and shelf-life, if not accounted for in the model. Finally, the study shed some light on how accuracy for the growth rate estimates might be improved by allocating more weight to observations prone to favorable than unfavorable environmental conditions.

Properties of the iron-sulfur cluster electron transfer relay in an [FeFe]-hydrogenase that is tuned for H2 oxidation catalysis

[FeFe]-hydrogenases catalyze the reversible oxidation of H2 from electrons and protons at an organometallic active site cofactor named the H-cluster. In addition to the H-cluster, most [FeFe]-hydrogenases possess accessory FeS cluster (F-cluster) relays that function in mediating electron transfer with catalysis. There is significant variation in the structural properties of F-cluster relays amongst the [FeFe]-hydrogenases, however it is unknown how this variation relates to the electronic and thermodynamic properties, and thus the electron transfer properties, of F-cluster relays. Clostridium pasteurianum [FeFe]-hydrogenase II (CpII), exhibits a large catalytic bias for H2 oxidation (compared to H2 production), making it a notable system for examining if F-cluster properties contribute to the overall function and efficiency of the enzyme. By applying a combination of multifrequency and potentiometric EPR, we resolved two EPR signals with distinct power- and temperature-dependent properties at g = 2.058 1.931 1.891 (F2.058) and g = 2.061 1.920 1.887 (F2.061), with assigned midpoint potentials of -140 ± 18 mV and -406 ± 12 mV vs. NHE, respectively. Spectral analysis revealed features consistent with spin-spin coupling between the two [4Fe-4S] F-clusters, and possible functional models are discussed that account for the contribution of coupling to the electron transfer landscape. The results signify the interplay of electronic coupling and free energy properties and parameters of the FeS clusters to the electron transfer mechanism through the relay and provide new insight as to how relays functionally complement the catalytic directionality of active sites to achieve highly efficient catalysis.

Persistent climate model biases in the Atlantic Ocean's freshwater transport

Abstract. The Atlantic Meridional Overturning Circulation (AMOC) is considered to be one of the most dangerous climate tipping elements. The salt–advection feedback plays an important role in AMOC tipping behaviour, and its strength is strongly connected to the freshwater transport carried by the AMOC at 34° S, below indicated by FovS. Available observations have indicated that FovS has a negative sign for the present-day AMOC. However, most climate models of the Coupled Model Intercomparison Project (CMIP, phase 3 and phase 5) have an incorrect FovS sign. Here, we analyse a high-resolution and a low-resolution version of the Community Earth System Model (CESM) to identify the origin of these FovS biases. Both CESM versions are initialised from an observed ocean state, and FovS biases quickly develop under fixed pre-industrial forcing conditions. The most important model bias is a too fresh Atlantic Surface Water, which arises from deficiencies in the surface freshwater flux over the Indian Ocean. The second largest bias is a too saline North Atlantic Deep Water and arises through deficiencies in the freshwater flux over the Atlantic Subpolar Gyre region. Climate change scenarios branched from the pre-industrial simulations have an incorrect FovS upon initialisation. Most CMIP phase 6 models have similar biases to those in the CESM. Due to the biases, the value of FovS is not in agreement with available observations, and the strength of the salt advection feedback is underestimated. Values of FovS are projected to decrease under climate change, and their response is also dependent on the various model biases. To better project future AMOC behaviour, an urgent effort is needed to reduce biases in the atmospheric components of current climate models.

Maximizing carbon sequestration potential in Chinese forests through optimal management

Forest carbon sequestration capacity in China remains uncertain due to underrepresented tree demographic dynamics and overlooked of harvest impacts. In this study, we employ a process-based biogeochemical model to make projections by using national forest inventories, covering approximately 415,000 permanent plots, revealing an expansion in biomass carbon stock by 13.6 ± 1.5 Pg C from 2020 to 2100, with additional sink through augmentation of wood product pool (0.6-2.0 Pg C) and spatiotemporal optimization of forest management (2.3 ± 0.03 Pg C). We find that statistical model might cause large bias in long-term projection due to underrepresentation or neglect of wood harvest and forest demographic changes. Remarkably, disregarding the repercussions of harvesting on forest age can result in a premature shift in the timing of the carbon sink peak by 1–3 decades. Our findings emphasize the pressing necessity for the swift implementation of optimal forest management strategies for carbon sequestration enhancement.