Siting Background Towers to Characterize Incoming Air for Urban Greenhouse Gas Estimation: A Case Study in the Washington, DC/Baltimore Area

There is increased interest in understanding urban greenhouse gas (GHG) emissions. To accurately estimate city emissions, the influence of extraurban fluxes must first be removed from urban greenhouse gas (GHG) observations. This is especially true for regions, such as the U.S. Northeastern Corridorâ Baltimore/Washington, DC (NECâ B/W), downwind of large fluxes. To help site background towers for the NECâ B/W, we use a coupled Bayesian Information Criteria and geostatistical regression approach to help site four background locations that best explain CO2 variability due to extraurban fluxes modeled at 12 urban towers. The synthetic experiment uses an atmospheric transport and dispersion model coupled with two different flux inventories to create modeled observations and evaluate 15 candidate towers located along the urban domain for February and July 2013. The analysis shows that the average ratios of extraurban inflow to total modeled enhancements at urban towers are 21% to 36% in February and 31% to 43% in July. In July, the incoming air dominates the total variability of synthetic enhancements at the urban towers (R2 = 0.58). Modeled observations from the selected background towers generally capture the variability in the synthetic CO2 enhancements at urban towers (R2 = 0.75, rootâ meanâ square error (RMSE) = 3.64 ppm; R2 = 0.43, RMSE = 4.96 ppm for February and July). However, errors associated with representing background air can be up to 10 ppm for any given observation even with an optimal background tower configuration. More sophisticated methods may be necessary to represent background air to accurately estimate urban GHG emissions.Key PointsFactoring in the variability of greenhouse gas enhancements in incoming air is critical for estimating emissions in an urban domainStatistical methods were used to site four towers sampling background air in the Washington, DC/Baltimore regionOptimal background tower configurations for representing incoming air can still have large errors for any given urban GHG observationPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142902/1/jgrd54353_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142902/2/jgrd54353.pd

A LOFAR prompt search for radio emission accompanying X-ray flares in GRB 210112A

© The Author(s) 2023. Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).The composition of relativistic gamma-ray burst (GRB) jets and their emission mechanisms are still debated, and they could be matter or magnetically dominated. One way to distinguish these mechanisms arises because a Poynting flux dominated jet may produce low-frequency radio emission during the energetic prompt phase, through magnetic reconnection at the shock front. We present a search for radio emission coincident with three GRB X-ray flares with the LOw Frequency ARray (LOFAR), in a rapid response mode follow-up of long GRB 210112A (at z~2) with a 2 hour duration, where our observations began 511 seconds after the initial swift-BAT trigger. Using timesliced imaging at 120-168 MHz, we obtain upper limits at 3 sigma confidence of 42 mJy averaging over 320 second snapshot images, and 87 mJy averaging over 60 second snapshot images. LOFAR's fast response time means that all three potential radio counterparts to X-ray flares are observable after accounting for dispersion at the estimated source redshift. Furthermore, the radio pulse in the magnetic wind model was expected to be detectable at our observing frequency and flux density limits which allows us to disfavour a region of parameter space for this GRB. However, we note that stricter constraints on redshift and the fraction of energy in the magnetic field are required to further test jet characteristics across the GRB population.Peer reviewe

Are health care professionals able to judge cancer patients' health care preferences correctly? A cross-sectional study

Background: Health care for cancer patients is primarily shaped by health care professionals. This raises the question to what extent health care professionals are aware of patients' preferences, needs and values. The aim of this study was to explore to what extent there is concordance between patients' preferences in cancer care and patients' preferences as estimated by health care professionals. We also examined whether there were gender differences between health care professionals with regard to the degree in which they can estimate patients' preferences correctly. Methods: To obtain unbiased insight into the specific preferences of cancer patients, we developed the 'Cancer patients' health care preferences' questionnaire'. With this questionnaire we assessed a large sample of cancer patients (n = 386). Next, we asked health care professionals (medical oncologists, nurses and policymakers, n = 60) to fill out this questionnaire and to indicate preferences they thought cancer patients would have. Mean scores between groups were compared using Mann-Whitney tests. Effect sizes (ESs) were calculated for statistically significant differences. Results: We found significant differences (ESs 0.31 to 0.90) between patients and professionals for eight out of twenty-one scales and two out of eight single items. Patients valued care aspects related to expertise and attitude of health care providers and accessibility of services as more important than the professionals thought they would do. Health care professionals overestimated the value that patients set on particularly organisational and environmental aspects. We found significant gender-related differences between the professionals (ESs 0.69 to 1.39) for eight out of twenty-one scales and two out of eight single items. When there were significant differences between male and female healthcare professionals in their estimation of patients health care preferences, female health care professionals invariably had higher scores. Generally, female health care professionals did not estimate patients' preferences and needs better than their male colleagues. Conclusions: Health care professionals are reasonably well able to make a correct estimation of patients preferences, but they should be aware of their own bias and use additional resources to gain a better understanding of patients' specific preferences for each patient is different and ultimately the care needs and preferences will also be unique to the person

Distribution and determinants of patient satisfaction in oncology with a focus on health related quality of life

<p>Abstract</p> <p>Background</p> <p>Cancer patients usually undergo extensive and debilitating treatments, which make quality of life (QoL) and patient satisfaction important health care assessment measures. However, very few studies have evaluated the relationship between QoL and patient satisfaction in oncology. We investigated the clinical, demographic and QoL factors associated with patient satisfaction in a large heterogeneous sample of cancer patients.</p> <p>Methods</p> <p>A cohort of 538 cancer patients treated at Cancer Treatment Centers of America^®(CTCA) was assessed. A patient satisfaction questionnaire developed in-house by CTCA was used. It covered the following dimensions of patient satisfaction: hospital operations and services, physicians and staff, and patient endorsements for themselves and others. QoL was assessed using the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (QLQ-C30). The clinical, demographic and QoL factors were evaluated for predictive significance using univariate and multivariate logistic regression.</p> <p>Results</p> <p>The mean age of our patient population was 54.1 years (SD = 10.5, range 17-86), with a slight preponderance of females (57.2%). Breast cancer (n = 124) and lung cancer (n = 101) were the most frequent cancer types. 481 (89.4%) patients were "very satisfied" with their overall experience. Age and several QoL function and symptom scales were predictive of overall patient satisfaction upon univariate analysis. In the multivariate modeling, only those with a score above the median on the fatigue measure (i.e. worse fatigue) had reduced odds of 0.28 of being very satisfied (p = 0.03).</p> <p>Conclusion</p> <p>Patient fatigue, as reported by the QoL fatigue scale, was an independent significant predictor of overall patient satisfaction. This finding argues for special attention and programs for cancer patients who report higher levels of fatigue given that fatigue is the most frequently reported symptom in cancer patients.</p

Regional-scale geostatistical inverse modeling of North American CO₂ fluxes: a synthetic data study

A series of synthetic data experiments is performed to investigate the ability of a regional atmospheric inversion to estimate grid-scale CO₂ fluxes during the growing season over North America. The inversions are performed within a geostatistical framework without the use of any prior flux estimates or auxiliary variables, in order to focus on the atmospheric constraint provided by the nine towers collecting continuous, calibrated CO₂ measurements in 2004. Using synthetic measurements and their associated concentration footprints, flux and model-data mismatch covariance parameters are first optimized, and then fluxes and their uncertainties are estimated at three different temporal resolutions. These temporal resolutions, which include a four-day average, a four-day-average diurnal cycle with 3-hourly increments, and 3-hourly fluxes, are chosen to help assess the impact of temporal aggregation errors on the estimated fluxes and covariance parameters. Estimating fluxes at a temporal resolution that can adjust the diurnal variability is found to be critical both for recovering covariance parameters directly from the atmospheric data, and for inferring accurate ecoregion-scale fluxes. Accounting for both spatial and temporal a priori covariance in the flux distribution is also found to be necessary for recovering accurate a posteriori uncertainty bounds on the estimated fluxes. Overall, the results suggest that even a fairly sparse network of 9 towers collecting continuous CO₂ measurements across the continent, used with no auxiliary information or prior estimates of the flux distribution in time or space, can be used to infer relatively accurate monthly ecoregion scale CO₂ surface fluxes over North America within estimated uncertainty bounds. Simulated random transport error is shown to decrease the quality of flux estimates in under-constrained areas at the ecoregion scale, although the uncertainty bounds remain realistic. While these synthetic data inversions do not consider all potential issues associated with using actual measurement data, e.g. systematic transport errors or problems with the boundary conditions, they help to highlight the impact of inversion setup choices, and help to provide a baseline set of CO₂ fluxes for comparison with estimates from future real-data inversions

Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study

International audienceGreenhouse gas emissions mitigation requires understanding the dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emission sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted down-wind methane concentrations given the same emissions field. The dispersion models tested often underpredicted the observed methane enhancements with significant variability (up to a factor of 3) between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion , indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions

The interaction of caerulein with the rat pancreas. II. Specific binding of [3H]caerulein on dispersed acinar cells

1. [3H]Caerulein was bound to dispersed acinar cells from rat pancreas in a rapid, reversible, specific, saturable, and temperature-dependent manner. Binding decreased above pH 6.5. Treatment of intact cells with 2, 4-dinitrophenol and oligomycin, p-choloromercuribenzoate, diisopropylfluoro-phosphate and glutaraldehyde impaired [3H]caerulein binding whereas the addition of EGTA inhibited binding. The C-terminal octapeptide of pancreozymin, desulfated caerulein and pentagastrin inhibited binding of [3H]caerulein whereas vasoactive intestinal polypeptide, secretin, bombesin or carbamoylcholine were wothout effect. The good resistance of [3H]caerulein to inactivation by acinar cells at 37 degrees C was reflected in the high proportion of tracer remaining capable of binding to fresh acinar cells. 2. Scatchard plots of [3H]caerulein binding were curvilinear with an upward concavity. The addition of an excess of unlabeled caerulein resulted in the release of as much as 65% of bound [3H]caerulein within 1 min at 37 degrees C. The dissociation of remainder followed much slower kinetics. 3. The results suggested that intact rat pancreatic acinar cells have one class of caerulein binding sites existing in two states: one with high affinity and another with low affinity, the proportion of sites in each state depending on the degree of site occupancy (negative cooperativity), and on the intracellular concentration of nucleotides.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

The Impact of COVID-19 on CO2 Emissions in the Los Angeles and Washington DC/Baltimore Metropolitan Areas

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course-correct CO2 reduction activities efficiently. © 2021. The Authors. This article has been contributed to by US Government employees and their work is in the public domain in the USA.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A LOFAR prompt search for radio emission accompanying X-ray flares in GRB 210112A

The composition of relativistic gamma-ray burst (GRB) jets and their emission mechanisms are still debated, and they could be matter or magnetically dominated. One way to distinguish these mechanisms arises because a Poynting flux dominated jet may produce low-frequency radio emission during the energetic prompt phase, through magnetic reconnection at the shock front. We present a search for radio emission coincident with three GRB X-ray flares with the LOw Frequency ARray (LOFAR), in a rapid response mode follow-up of long GRB 210112A (at z ∼ 2) with a 2 h duration, where our observations began 511 s after the initial Swift-BAT trigger. Using time-sliced imaging at 120–168 MHz, we obtain upper limits at 3σ confidence of 42 mJy averaging over 320 s snapshot images, and 87 mJy averaging over 60 s snapshot images. LOFAR’s fast response time means that all three potential radio counterparts to X-ray flares are observable after accounting for dispersion at the estimated source redshift. Furthermore, the radio pulse in the magnetic wind model was expected to be detectable at our observing frequency and flux density limits which allows us to disfavour a region of parameter space for this GRB. However, we note that stricter constraints on redshift and the fraction of energy in the magnetic field are required to further test jet characteristics across the GRB population.</p