The role of large-scale atmospheric circulations on long-term variations of PM10 concentrations over Turkey.

PM10 is widely identified as an important atmospheric pollutant posing a serious threat to human health and environment as well as it influences the climate system. To unearth the mechanism involved in its sources and circulation behavior in environment, this study focuses on the role of large-scale atmospheric circulation on the long-term variability of PM10 over Turkey by applying rotated empirical orthogonal functions (REOF) analysis. As a result of the implementation of REOF to the daily PM10 data for 80 air quality stations throughout the period 2010-2020, first REOF mode (REOF1 44.9% in winter, 43.2% in spring, 39.5% in summer and 31.6% in fall) for all the four seasons indicated the role of local emission sources on the variations of PM10, which show high PM10 values in different geographical regions. The results of the second mode (REOF2, 17.9% in winter, 14.0% in spring, 14.0% in summer and 16.3% in fall) indicate the role of large-scale atmospheric circulations on the values of PM10. From the REOF2 analysis and extracted synoptic composite maps, the strength of southerly winds and the presence of southwesterly winds at low levels are very important in transporting of dust pollutants from the Arabian Peninsula and Northern Africa, respectively, to the eastern (EAR) and southeastern (SEAR) regions of Turkey during winter. In spring, sand particles in the interior terrestrial part of the country are carried to the northern regions by the effect of large-scale southerly winds, which cause above-normal PM10 concentrations in the Black Sea region of Turkey. In summer, dust particles together with warm dry air intrusion to the eastern region of Turkey by strong easterly winds are sourced by Caspian Sea and result in high PM10 values. Our findings emphasize that the long-term variations in air quality over Turkey are affected secondary by the variations in the large-scale atmospheric circulations with primary contributions from the changes in local emission sources.

Impacts of exposure to air pollution, radon and climate drivers on the COVID-19 pandemic in Bucharest, Romania: A time series study.

During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus' effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO2, sulfur dioxide-SO2, carbon monoxide-CO) and radon - 222Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O3 levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.

Assessing the impact of air pollution and climate seasonality on COVID-19 multiwaves in Madrid, Spain.

While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020-1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future.

An Effective and Robust Approach Based on R-CNN+LSTM Model and NCAR Feature Selection for Ophthalmological Disease Detection from Fundus Images.

Changes in and around anatomical structures such as blood vessels, optic disc, fovea, and macula can lead to ophthalmological diseases such as diabetic retinopathy, glaucoma, age-related macular degeneration (AMD), myopia, hypertension, and cataracts. If these diseases are not diagnosed early, they may cause partial or complete loss of vision in patients. Fundus imaging is the primary method used to diagnose ophthalmologic diseases. In this study, a powerful R-CNN+LSTM-based approach is proposed that automatically detects eight different ophthalmologic diseases from fundus images. Deep features were extracted from fundus images with the proposed R-CNN+LSTM structure. Among the deep features extracted, those with high representative power were selected with an approach called NCAR, which is a multilevel feature selection algorithm. In the classification phase, the SVM algorithm, which is a powerful classifier, was used. The proposed approach is evaluated on the eight-class ODIR dataset. The accuracy (main metric), sensitivity, specificity, and precision metrics were used for the performance evaluation of the proposed approach. Besides, the performance of the proposed approach was compared with the existing approaches using the ODIR dataset.

[Emission Inventory of Intermediate Volatility Organic Compounds(IVOCs) from Biomass Burning in the Yangtze River Delta During 2010-2018].

Intermediate volatility organic compounds (IVOCs) are important precursors of secondary organic aerosols (SOA) but are currently not included in the conventional emissions inventories. Biomass burning represents an important source of IVOCs that could contribute to SOA formation. This study estimated the IVOC emissions from biomass burning in the Yangtze River Delta (YRD) region from 2010 to 2018 based on the fire inventory from NCAR (FINN) and the IVOCs/primary organic aerosol (POA) ratio reported in literature. During this period, the total number of fire events over the YRD region presented a declining trend, with an average of 104 fire events detected per year. During 2016-2018, the average number of fire events was approximately 6000 per year, which was 60% less than that prior to 2016. In terms of the monthly variation, the period from May to August was the period with the most fires observed, which was followed by a small peak in October. The results calculated based on the IVOCs/POA ratio method showed that the IVOC emissions from biomass burning exhibited large differences with different combinations of POA/OC and IVOCs/POA ratios, ranging from a maximum of 305.7×104 t to as small as 10.5×104 t. Monte Carlo simulation revealed that the uncertainties associated with the IVOCs/POA ratio method range from -99% to 68%.

Differences in Perspectives of Medical Device Adverse Events: Observational Results in Training Program Using Virtual Cases.

BACKGROUND: Medical device adverse event reporting is an essential activity for mitigating device-related risks. Reporting of adverse events can be done by anyone like healthcare workers, patients, and others. However, for an individual to determine the reporting, he or she should recognize the current situation as an adverse event. The objective of this report is to share observed individual differences in the perception of a medical device adverse event, which may affect the judgment and the reporting of adverse events. METHODS: We trained twenty-three participants from twelve Asia-Pacific Economic Cooperation (APEC) member economies about international guidelines for medical device vigilance. We developed and used six virtual cases and six questions. We divided participants into six groups and compared their opinions. We also surveyed the country's opinion to investigate the beginning point of 'patient use'. The phases of 'patient use' are divided into: 1) inspecting, 2) preparing, and 3) applying medical device. RESULTS: As for the question on the beginning point of 'patient use,' 28.6%, 35.7%, and 35.7% of participants provided answers regarding the first, second, and third phases, respectively. In training for applying international guidelines to virtual cases, only one of the six questions reached a consensus between the two groups in all six virtual cases. For the other five questions, different judgments were given in at least two groups. CONCLUSION: From training courses using virtual cases, we found that there was no consensus on 'patient use' point of view of medical devices. There was a significant difference in applying definitions of adverse events written in guidelines regarding the medical device associated incidents. Our results point out that international harmonization effort is needed not only to harmonize differences in regulations between countries but also to overcome diversity in perspectives existing at the site of medical device use.

Differences in Perspectives of Medical Device Adverse Events: Observational Results in Training Program Using Virtual Cases

BACKGROUND: Medical device adverse event reporting is an essential activity for mitigating device-related risks. Reporting of adverse events can be done by anyone like healthcare workers, patients, and others. However, for an individual to determine the reporting, he or she should recognize the current situation as an adverse event. The objective of this report is to share observed individual differences in the perception of a medical device adverse event, which may affect the judgment and the reporting of adverse events. METHODS: We trained twenty-three participants from twelve Asia-Pacific Economic Cooperation (APEC) member economies about international guidelines for medical device vigilance. We developed and used six virtual cases and six questions. We divided participants into six groups and compared their opinions. We also surveyed the country's opinion to investigate the beginning point of ‘patient use’. The phases of ‘patient use’ are divided into: 1) inspecting, 2) preparing, and 3) applying medical device. RESULTS: As for the question on the beginning point of ‘patient use,’ 28.6%, 35.7%, and 35.7% of participants provided answers regarding the first, second, and third phases, respectively. In training for applying international guidelines to virtual cases, only one of the six questions reached a consensus between the two groups in all six virtual cases. For the other five questions, different judgments were given in at least two groups. CONCLUSION: From training courses using virtual cases, we found that there was no consensus on ‘patient use’ point of view of medical devices. There was a significant difference in applying definitions of adverse events written in guidelines regarding the medical device associated incidents. Our results point out that international harmonization effort is needed not only to harmonize differences in regulations between countries but also to overcome diversity in perspectives existing at the site of medical device use.

Development of R packages: 'NonCompart' and 'ncar' for noncompartmental analysis (NCA).

Noncompartmental analysis (NCA) is a primary analytical approach for pharmacokinetic studies, and its parameters act as decision criteria in bioequivalent studies. Currently, NCA is usually carried out by commercial softwares such as WinNonlin®. In this article, we introduce our newly-developed two R packages, NonCompart (NonCompartmental analysis for pharmacokinetic data) and ncar (NonCompartmental Analysis for pharmacokinetic Report), which can perform NCA and produce complete NCA reports in both pdf and rtf formats. These packages are compatible with CDISC (Clinical Data Interchange Standards Consortium) standard as well. We demonstrate how the results of WinNonlin® are reproduced and how NCA reports can be obtained. With these R packages, we aimed to help researchers carry out NCA and utilize the output for early stages of drug development process. These R packages are freely available for download from the CRAN repository.

Development of R packages: ‘NonCompart’ and ‘ncar’ for noncompartmental analysis (NCA)

Noncompartmental analysis (NCA) is a primary analytical approach for pharmacokinetic studies, and its parameters act as decision criteria in bioequivalent studies. Currently, NCA is usually carried out by commercial softwares such as WinNonlin®. In this article, we introduce our newly-developed two R packages, NonCompart (NonCompartmental analysis for pharmacokinetic data) and ncar (NonCompartmental Analysis for pharmacokinetic Report), which can perform NCA and produce complete NCA reports in both pdf and rtf formats. These packages are compatible with CDISC (Clinical Data Interchange Standards Consortium) standard as well. We demonstrate how the results of WinNonlin® are reproduced and how NCA reports can be obtained. With these R packages, we aimed to help researchers carry out NCA and utilize the output for early stages of drug development process. These R packages are freely available for download from the CRAN repository.

Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4.

Several studies have shown that satellite retrievals of solar-induced chlorophyll fluorescence (SIF) provide useful information on terrestrial photosynthesis or gross primary production (GPP). Here, we have incorporated equations coupling SIF to photosynthesis in a land surface model, the National Center for Atmospheric Research Community Land Model version 4 (NCAR CLM4), and have demonstrated its use as a diagnostic tool for evaluating the calculation of photosynthesis, a key process in a land surface model that strongly influences the carbon, water, and energy cycles. By comparing forward simulations of SIF, essentially as a byproduct of photosynthesis, in CLM4 with observations of actual SIF, it is possible to check whether the model is accurately representing photosynthesis and the processes coupled to it. We provide some background on how SIF is coupled to photosynthesis, describe how SIF was incorporated into CLM4, and demonstrate that our simulated relationship between SIF and GPP values are reasonable when compared with satellite (Greenhouse gases Observing SATellite; GOSAT) and in situ flux-tower measurements. CLM4 overestimates SIF in tropical forests, and we show that this error can be corrected by adjusting the maximum carboxylation rate (Vmax ) specified for tropical forests in CLM4. Our study confirms that SIF has the potential to improve photosynthesis simulation and thereby can play a critical role in improving land surface and carbon cycle models.