scholarly journals Improved monitoring of shipping NO<sub>2</sub> with TROPOMI: decreasing NO<sub>x</sub> emissions in European seas during the COVID-19 pandemic

2021 ◽  
Author(s):  
T. Christoph V. W. Riess ◽  
K. Folkert Boersma ◽  
Jasper van Vliet ◽  
Wouter Peters ◽  
Maarten Sneep ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Information Quality ◽  
Marine Boundary Layer ◽  
Aegean Sea ◽  
Data Set ◽  
Lower Cloud ◽  
Optimal Data Selection ◽  
The Impact ◽  
No2 Pollution ◽  
European Seas

Abstract. TROPOMI measurements of tropospheric NO2 columns provide powerful information on emissions of air pollution by ships on open sea. This information is potentially useful for authorities to help determine the (non-)compliance of ships with increasingly stringent NOx emission regulations. We find that the information quality is improved further by recent upgrades in the TROPOMI cloud retrieval and an optimal data selection. We show that the superior spatial resolution of TROPOMI allows the detection of several lanes of NO2 pollution ranging from the Aegean Sea near Greece to the Skagerrak in Scandinavia, which have not been detected with other satellite instruments before. Additionally, we demonstrate that under conditions of sun glint TROPOMI's vertical sensitivity to NO2 in the marine boundary layer increases by up to 60 %. The benefits of sun glint are most prominent under clear-sky situations when sea surface winds are low, but slightly above zero (±2 m/s). Beyond spatial resolution and sun glint, we examine for the first time the impact of the recently improved cloud algorithm on the TROPOMI NO2 retrieval quality, both over sea and over land. We find that the new FRESCO+wide algorithm leads to 50 hPa lower cloud pressures, correcting a known high bias, and produces 1–4·1015 molec/cm2 higher retrieved NO2 columns, thereby at least partially correcting for the previously reported low bias in the TROPOMI NO2 product. By training an artificial neural network on the 4 available periods with standard and FRESCO+wide test-retrievals, we develop a historic, consistent TROPOMI NO2 data set spanning the years 2019 and 2020. This improved data set shows stronger (35–75 %) and sharper (10–35 %) shipping NO2 signals compared to co-sampled measurements from OMI. We apply our improved data set to investigate the impact of the COVID-19 pandemic on ship NO2 pollution over European seas and find indications that NOx emissions from ships reduced by 20–25 % during the pandemic. The reductions in ship NO2 pollution start in March–April 2020, in line with changes in shipping activity inferred from AIS data.

scholarly journals Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations

2016 ◽  
Vol 20 (7) ◽  
pp. 3059-3076 ◽  
Author(s):  
Patricia López López ◽  
Niko Wanders ◽  
Jaap Schellekens ◽  
Luigi J. Renzullo ◽  
Edwin H. Sutanudjaja ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Observation System ◽  
Data Set ◽  
Coarse Resolution ◽  
The Impact

Abstract. The coarse spatial resolution of global hydrological models (typically  >  0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (downscaled from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). Downscaled satellite-derived soil moisture (downscaled from  ∼  0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse-resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently made to move to global hyper-resolution modelling and can help to advance this research.

Age at Exposure to Parental Suicide and the Subsequent Risk of Suicide in Young People

Crisis ◽  
2018 ◽  
Vol 39 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Kuan-Ying Lee ◽  
Chung-Yi Li ◽  
Kun-Chia Chang ◽  
Tsung-Hsueh Lu ◽  
Ying-Yeh Chen
Keyword(s):  
Young People ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Birth Registry ◽  
Data Set ◽  
Parental Suicide ◽  
The Impact ◽  
Age At Exposure

Abstract. Background: We investigated the age at exposure to parental suicide and the risk of subsequent suicide completion in young people. The impact of parental and offspring sex was also examined. Method: Using a cohort study design, we linked Taiwan's Birth Registry (1978–1997) with Taiwan's Death Registry (1985–2009) and identified 40,249 children who had experienced maternal suicide (n = 14,431), paternal suicide (n = 26,887), or the suicide of both parents (n = 281). Each exposed child was matched to 10 children of the same sex and birth year whose parents were still alive. This yielded a total of 398,081 children for our non-exposed cohort. A Cox proportional hazards model was used to compare the suicide risk of the exposed and non-exposed groups. Results: Compared with the non-exposed group, offspring who were exposed to parental suicide were 3.91 times (95% confidence interval [CI] = 3.10–4.92 more likely to die by suicide after adjusting for baseline characteristics. The risk of suicide seemed to be lower in older male offspring (HR = 3.94, 95% CI = 2.57–6.06), but higher in older female offspring (HR = 5.30, 95% CI = 3.05–9.22). Stratified analyses based on parental sex revealed similar patterns as the combined analysis. Limitations: As only register-­based data were used, we were not able to explore the impact of variables not contained in the data set, such as the role of mental illness. Conclusion: Our findings suggest a prominent elevation in the risk of suicide among offspring who lost their parents to suicide. The risk elevation differed according to the sex of the afflicted offspring as well as to their age at exposure.

State Medical Boards' Perceptions of a Minimum Data Set and Current Practices for Collecting Physician Information

2013 ◽  
Vol 99 (4) ◽  
pp. 40-45 ◽  
Author(s):  
Aaron Young ◽  
Philip Davignon ◽  
Margaret B. Hansen ◽  
Mark A. Eggen
Keyword(s):  
Media Coverage ◽  
The United States ◽  
Minimum Data Set ◽  
Direct Patient Care ◽  
Physician Workforce ◽  
Data Set ◽  
Minimum Data ◽  
State Medical Boards ◽  
The Impact ◽  
Current Practices

ABSTRACT Recent media coverage has focused on the supply of physicians in the United States, especially with the impact of a growing physician shortage and the Affordable Care Act. State medical boards and other entities maintain data on physician licensure and discipline, as well as some biographical data describing their physician populations. However, there are gaps of workforce information in these sources. The Federation of State Medical Boards' (FSMB) Census of Licensed Physicians and the AMA Masterfile, for example, offer valuable information, but they provide a limited picture of the physician workforce. Furthermore, they are unable to shed light on some of the nuances in physician availability, such as how much time physicians spend providing direct patient care. In response to these gaps, policymakers and regulators have in recent years discussed the creation of a physician minimum data set (MDS), which would be gathered periodically and would provide key physician workforce information. While proponents of an MDS believe it would provide benefits to a variety of stakeholders, an effort has not been attempted to determine whether state medical boards think it is important to collect physician workforce data and if they currently collect workforce information from licensed physicians. To learn more, the FSMB sent surveys to the executive directors at state medical boards to determine their perceptions of collecting workforce data and current practices regarding their collection of such data. The purpose of this article is to convey results from this effort. Survey findings indicate that the vast majority of boards view physician workforce information as valuable in the determination of health care needs within their state, and that various boards are already collecting some data elements. Analysis of the data confirms the potential benefits of a physician minimum data set (MDS) and why state medical boards are in a unique position to collect MDS information from physicians.

The Impact of Green Labels on U.S. Hotel Net Operating Income: Operating Statements Analyses

2019 ◽  
Vol 11 (1) ◽  
pp. 156-173
Author(s):  
Spenser Robinson ◽  
A.J. Singh
Keyword(s):  
Environmental Design ◽  
Green Buildings ◽  
Small Sample ◽  
Sample Survey ◽  
Data Set ◽  
Operating Income ◽  
Energy Star ◽  
The Impact ◽  
Industry Professionals

This paper shows Leadership in Energy and Environmental Design (LEED) certified hospitality properties exhibit increased expenses and earn lower net operating income (NOI) than non-certified buildings. ENERGY STAR certified properties demonstrate lower overall expenses than non-certified buildings with statistically neutral NOI effects. Using a custom sample of all green buildings and their competitive data set as of 2013 provided by Smith Travel Research (STR), the paper documents potential reasons for this result including increased operational expenses, potential confusion with certified and registered LEED projects in the data, and qualitative input. The qualitative input comes from a small sample survey of five industry professionals. The paper provides one of the only analyses on operating efficiencies with LEED and ENERGY STAR hospitality properties.

Does Tax Increment Financing Pass the “But-for” Test in Missouri?

2019 ◽  
Vol 33 (3) ◽  
pp. 187-202
Author(s):  
Ahmed Rachid El-Khattabi ◽  
T. William Lester
Keyword(s):  
Economic Development ◽  
Kansas City ◽  
Time Series Data ◽  
Local Level ◽  
Conclusive Evidence ◽  
Series Data ◽  
Tax Increment Financing ◽  
Data Set ◽  
Development Indicators ◽  
The Impact

The use of tax increment financing (TIF) remains a popular, yet highly controversial, tool among policy makers in their efforts to promote economic development. This study conducts a comprehensive assessment of the effectiveness of Missouri’s TIF program, specifically in Kansas City and St. Louis, in creating economic opportunities. We build a time-series data set starting 1990 through 2012 of detailed employment levels, establishment counts, and sales at the census block-group level to run a set of difference-in-differences with matching estimates for the impact of TIF at the local level. Although we analyze the impact of TIF on a wide set of indicators and across various industry sectors, we find no conclusive evidence that the TIF program in either city has a causal impact on key economic development indicators.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Efficacy and safety of nintedanib in patients with idiopathic pulmonary fibrosis who are elderly or have comorbidities

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ian Glaspole ◽  
Francesco Bonella ◽  
Elena Bargagli ◽  
Marilyn K. Glassberg ◽  
Fabian Caro ◽  
...  
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Adverse Events ◽  
Treatment Discontinuation ◽  
Efficacy And Safety ◽  
Data Set ◽  
Proactive Management ◽  
Comorbidity Burden ◽  
Rate Of Decline ◽  
The Impact

Abstract Background Idiopathic pulmonary fibrosis (IPF) predominantly affects individuals aged > 60 years who have several comorbidities. Nintedanib is an approved treatment for IPF, which reduces the rate of decline in forced vital capacity (FVC). We assessed the efficacy and safety of nintedanib in patients with IPF who were elderly and who had multiple comorbidities. Methods Data were pooled from five clinical trials in which patients were randomised to receive nintedanib 150 mg twice daily or placebo. We assessed outcomes in subgroups by age < 75 versus ≥ 75 years, by < 5 and ≥ 5 comorbidities, and by Charlson Comorbidity Index (CCI) ≤ 3 and > 3 at baseline. Results The data set comprised 1690 patients. Nintedanib reduced the rate of decline in FVC (mL/year) over 52 weeks versus placebo in patients aged ≥ 75 years (difference: 105.3 [95% CI 39.3, 171.2]) (n = 326) and < 75 years (difference 125.2 [90.1, 160.4]) (n = 1364) (p = 0.60 for treatment-by-time-by-subgroup interaction), in patients with < 5 comorbidities (difference: 107.9 [95% CI 65.0, 150.9]) (n = 843) and ≥ 5 comorbidities (difference 139.3 [93.8, 184.8]) (n = 847) (p = 0.41 for treatment-by-time-by-subgroup interaction) and in patients with CCI score ≤ 3 (difference: 106.4 [95% CI 70.4, 142.4]) (n = 1330) and CCI score > 3 (difference: 129.5 [57.6, 201.4]) (n = 360) (p = 0.57 for treatment-by-time-by-subgroup interaction). The adverse event profile of nintedanib was generally similar across subgroups. The proportion of patients with adverse events leading to treatment discontinuation was greater in patients aged ≥ 75 years than < 75 years in both the nintedanib (26.4% versus 16.0%) and placebo (12.2% versus 10.8%) groups. Similarly the proportion of patients with adverse events leading to treatment discontinuation was greater in patients with ≥ 5 than < 5 comorbidities (nintedanib: 20.5% versus 15.7%; placebo: 12.1% versus 10.0%). Conclusions Our findings suggest that the effect of nintedanib on reducing the rate of FVC decline is consistent across subgroups based on age and comorbidity burden. Proactive management of adverse events is important to reduce the impact of adverse events and help patients remain on therapy. Trial registration: ClinicalTrials.gov NCT00514683, NCT01335464, NCT01335477, NCT02788474, NCT01979952.

scholarly journals PROMIS Scales for Assessment of Persistent Post-COVID Symptoms: A Cross Sectional Study

2021 ◽  
Vol 12 ◽  
pp. 215013272110304
Author(s):  
Ravindra Ganesh ◽  
Aditya K. Ghosh ◽  
Mark A. Nyman ◽  
Ivana T. Croghan ◽  
Stephanie L. Grach ◽  
...  
Keyword(s):  
Health Status ◽  
Social Roles ◽  
Disease Process ◽  
Cross Sectional Study ◽  
Large Sample Size ◽  
Sectional Study ◽  
Cross Sectional ◽  
Data Set ◽  
Patient Reported ◽  
The Impact

Objective Persistent post-COVID symptoms are estimated to occur in up to 10% of patients who have had COVID-19. These lingering symptoms may persist for weeks to months after resolution of the acute illness. This study aimed to add insight into our understanding of certain post-acute conditions and clinical findings. The primary purpose was to determine the persistent post COVID impairments prevalence and characteristics by collecting post COVID illness data utilizing Patient-Reported Outcomes Measurement Information System (PROMIS®). The resulting measures were used to assess surveyed patients physical, mental, and social health status. Methods A cross-sectional study and 6-months Mayo Clinic COVID recovered registry data were used to evaluate continuing symptoms severity among the 817 positive tested patients surveyed between March and September 2020. The resulting PROMIS® data set was used to analyze patients post 30 days health status. The e-mailed questionnaires focused on fatigue, sleep, ability to participate in social roles, physical function, and pain. Results The large sample size (n = 817) represented post hospitalized and other managed outpatients. Persistent post COVID impairments prevalence and characteristics were determined to be demographically young (44 years), white (87%), and female (61%). Dysfunction as measured by the PROMIS® scales in patients recovered from acute COVID-19 was reported as significant in the following domains: ability to participate in social roles (43.2%), pain (17.8%), and fatigue (16.2%). Conclusion Patient response on the PROMIS® scales was similar to that seen in multiple other studies which used patient reported symptoms. As a result of this experience, we recommend utilizing standardized scales such as the PROMIS® to obtain comparable data across the patients’ clinical course and define the disease trajectory. This would further allow for effective comparison of data across studies to better define the disease process, risk factors, and assess the impact of future treatments.

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