scholarly journals Atmospheric Impacts of COVID-19 on NOx and VOC Levels over China Based on TROPOMI and IASI Satellite Data and Modeling

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 946
Author(s):  
Trissevgeni Stavrakou ◽  
Jean-François Müller ◽  
Maite Bauwens ◽  
Thierno Doumbia ◽  
Nellie Elguindi ◽  
...  
Keyword(s):  
Large Scale ◽  
Regional Model ◽  
Pollutant Emissions ◽  
Anthropogenic Emissions ◽  
Atmospheric Nitrogen ◽  
Model Simulations ◽  
Progressive Return ◽  
Oxygenated Volatile Organic Compounds ◽  
Volatile Organic ◽  
A Minor

China was the first country to undergo large-scale lockdowns in response to the pandemic in early 2020 and a progressive return to normalization after April 2020. Spaceborne observations of atmospheric nitrogen dioxide (NO2) and oxygenated volatile organic compounds (OVOCs), including formaldehyde (HCHO), glyoxal (CHOCHO), and peroxyacetyl nitrate (PAN), reveal important changes over China in 2020, relative to 2019, in response to the pandemic-induced shutdown and the subsequent drop in pollutant emissions. In February, at the peak of the shutdown, the observed declines in OVOC levels were generally weaker (less than 20%) compared to the observed NO2 reductions (−40%). In May 2020, the observations reveal moderate decreases in NO2 (−15%) and PAN (−21%), small changes in CHOCHO (−3%) and HCHO (6%). Model simulations using the regional model MAGRITTEv1.1 with anthropogenic emissions accounting for the reductions due to the pandemic explain to a large extent the observed changes in lockdown-affected regions. The model results suggest that meteorological variability accounts for a minor but non-negligible part (~−5%) of the observed changes for NO2, whereas it is negligible for CHOCHO but plays a more substantial role for HCHO and PAN, especially in May. The interannual variability of biogenic and biomass burning emissions also contribute to the observed variations, explaining e.g., the important column increases of NO2 and OVOCs in February 2020, relative to 2019. These changes are well captured by the model simulations.

scholarly journals Improving Regional Model Simulations with Precipitation Assimilation

2005 ◽  
Vol 9 (20) ◽  
pp. 1-44 ◽  
Author(s):  
Ana M. B. Nunes ◽  
John O. Roads
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Initial Conditions ◽  
Regional Model ◽  
Seasonal Forecasts ◽  
Wet Season ◽  
Model Simulations ◽  
Precipitation Simulation ◽  
Global Reanalysis ◽  
Model Precipitation

Abstract Although large-scale atmospheric reanalyses are now providing physical, realistic fields for many variables, precipitation remains problematic. As shown in recent studies, using a regional model to downscale the global reanalysis only marginally alleviates the precipitation simulation problems. For this reason, later-generation analyses, including the recent National Centers for Environmental Prediction regional reanalysis, are using precipitation assimilation as a methodology to provide superior precipitation fields. This methodology can also be applied to regional model simulations to substantially improve the precipitation fields downscaled from global reanalysis. As an illustration of the regional model precipitation assimilation impact, the authors describe here an extended-range simulation comparison over South America. The numerical experiments cover the beginning of the Large-Scale Biosphere–Atmosphere wet season campaign of January 1999. Evaluations using radiosonde datasets obtained during this campaign are provided as well. As will be shown, rain-rate assimilation not only increases the regional model precipitation simulation skill but also provides improvements in other fields influenced by the precipitation. Because of the potential impact on land surface features, the authors believe they will ultimately be able to show improvements in monthly to seasonal forecasts when precipitation assimilation is used to generate more accurate land surface initial conditions.

scholarly journals Improved simulation of regional CO<sub>2</sub> surface concentrations using GEOS-Chem and fluxes from VEGAS

2013 ◽  
Vol 13 (15) ◽  
pp. 7607-7618 ◽  
Author(s):  
Z. H. Chen ◽  
J. Zhu ◽  
N. Zeng
Keyword(s):  
North American ◽  
Large Scale ◽  
Transport Model ◽  
Co2 Concentration ◽  
Regional Model ◽  
Surface Fluxes ◽  
Persistent Problem ◽  
Model Simulations ◽  
Co2 Concentrations ◽  
Single Station

Abstract. CO2 measurements have been combined with simulated CO2 distributions from a transport model in order to produce the optimal estimates of CO2 surface fluxes in inverse modeling. However, one persistent problem in using model–observation comparisons for this goal relates to the issue of compatibility. Observations at a single station reflect all underlying processes of various scales. These processes usually cannot be fully resolved by model simulations at the grid points nearest the station due to lack of spatial or temporal resolution or missing processes in the model. In this study the stations in one region were grouped based on the amplitude and phase of the seasonal cycle at each station. The regionally averaged CO2 at all stations in one region represents the regional CO2 concentration of this region. The regional CO2 concentrations from model simulations and observations were used to evaluate the regional model results. The difference of the regional CO2 concentration between observation and modeled results reflects the uncertainty of the large-scale flux in the region where the grouped stations are. We compared the regional CO2 concentrations between model results with biospheric fluxes from the Carnegie-Ames-Stanford Approach (CASA) and VEgetation-Global-Atmosphere-Soil (VEGAS) models, and used observations from GLOBALVIEW-CO2 to evaluate the regional model results. The results show the largest difference of the regionally averaged values between simulations with fluxes from VEGAS and observations is less than 5 ppm for North American boreal, North American temperate, Eurasian boreal, Eurasian temperate and Europe, which is smaller than the largest difference between CASA simulations and observations (more than 5 ppm). There is still a large difference between two model results and observations for the regional CO2 concentration in the North Atlantic, Indian Ocean, and South Pacific tropics. The regionally averaged CO2 concentrations will be helpful for comparing CO2 concentrations from modeled results and observations and evaluating regional surface fluxes from different methods.

scholarly journals Improved simulation of group averaged CO<sub>2</sub> surface concentrations using GEOS-Chem and fluxes from VEGAS

2013 ◽  
Vol 13 (1) ◽  
pp. 2243-2271
Author(s):  
Z. H. Chen ◽  
J. Zhu ◽  
N. Zeng
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Surface Characteristics ◽  
Co2 Concentration ◽  
Regional Model ◽  
Surface Fluxes ◽  
Persistent Problem ◽  
Model Simulations ◽  
The Difference ◽  
Grid Points

Abstract. CO2 measurements have been combined with simulated CO2 distributions from a transport model in order to produce the optimal estimates of CO2 surface fluxes in inverse modeling. However one persistent problem in using model-observation comparisons for this goal relates to the issue of compatibility. Observations at a single site reflect all underlying processes of various scales that usually cannot be fully resolved by model simulations at the grid points nearest the site due to lack of spatial or temporal resolution or missing processes in models. In this article we group site observations of multiple stations according to atmospheric mixing regimes and surface characteristics. The group averaged values of CO2 concentration from model simulations and observations are used to evaluate the regional model results. Using the group averaged measurements of CO2 reduces the noise of individual stations. The difference of group averaged values between observation and modeled results reflects the uncertainties of the large scale flux in the region where the grouped stations are. We compared the group averaged values between model results with two biospheric fluxes from the model Carnegie-Ames-Stanford-Approach (CASA) and VEgetation-Global-Atmosphere-Soil (VEGAS) and observations to evaluate the regional model results. Results show that the modeling group averaged values of CO2 concentrations in all regions with fluxes from VEGAS have significant improvements for most regions. There is still large difference between two model results and observations for grouped average values in North Atlantic, Indian Ocean, and South Pacific Tropics. This implies possible large uncertainties in the fluxes there.

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

2020 ◽  
Vol 39 (4) ◽  
pp. 5449-5458
Author(s):  
A. Arokiaraj Jovith ◽  
S.V. Kasmir Raja ◽  
A. Razia Sulthana
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Control Parameter ◽  
Interference Mitigation ◽  
Wireless Sensor ◽  
Point To Point ◽  
A Minor ◽  
Two Stages ◽  
Time Division ◽  
And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

ON REGIONAL MODEL SIMULATIONS OF CLIMATE CHANGE OVER NEW ZEALAND

1999 ◽  
Vol 19 ◽  
pp. 3 ◽  
Author(s):  
Renwick ◽  
Katzfey ◽  
McGregor ◽  
Nguyen
Keyword(s):  
Climate Change ◽  
New Zealand ◽  
Regional Model ◽  
Model Simulations

scholarly journals Flavonoid-Modifying Capabilities of the Human Gut Microbiome—An In Silico Study

Nutrients ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 2688
Author(s):  
Tobias Goris ◽  
Rafael R. C. Cuadrat ◽  
Annett Braune
Keyword(s):  
Gut Microbiome ◽  
Large Scale ◽  
Health Impact ◽  
Gut Bacteria ◽  
Human Gut ◽  
Positive Health ◽  
Human Gut Microbiome ◽  
Nutritional Recommendations ◽  
Genes Encoding ◽  
A Minor

Flavonoids are a major group of dietary plant polyphenols and have a positive health impact, but their modification and degradation in the human gut is still widely unknown. Due to the rise of metagenome data of the human gut microbiome and the assembly of hundreds of thousands of bacterial metagenome-assembled genomes (MAGs), large-scale screening for potential flavonoid-modifying enzymes of human gut bacteria is now feasible. With sequences of characterized flavonoid-transforming enzymes as queries, the Unified Human Gastrointestinal Protein catalog was analyzed and genes encoding putative flavonoid-modifying enzymes were quantified. The results revealed that flavonoid-modifying enzymes are often encoded in gut bacteria hitherto not considered to modify flavonoids. The enzymes for the physiologically important daidzein-to-equol conversion, well studied in Slackiaisoflavoniconvertens, were encoded only to a minor extent in Slackia MAGs, but were more abundant in Adlercreutzia equolifaciens and an uncharacterized Eggerthellaceae species. In addition, enzymes with a sequence identity of about 35% were encoded in highly abundant MAGs of uncultivated Collinsella species, which suggests a hitherto uncharacterized daidzein-to-equol potential in these bacteria. Of all potential flavonoid modification steps, O-deglycosylation (including derhamnosylation) was by far the most abundant in this analysis. In contrast, enzymes putatively involved in C-deglycosylation were detected less often in human gut bacteria and mainly found in Agathobacter faecis (formerly Roseburia faecis). Homologs to phloretin hydrolase, flavanonol/flavanone-cleaving reductase and flavone reductase were of intermediate abundance (several hundred MAGs) and mainly prevalent in Flavonifractor plautii. This first comprehensive insight into the black box of flavonoid modification in the human gut highlights many hitherto overlooked and uncultured bacterial genera and species as potential key organisms in flavonoid modification. This could lead to a significant contribution to future biochemical-microbiological investigations on gut bacterial flavonoid transformation. In addition, our results are important for individual nutritional recommendations and for biotechnological applications that rely on novel enzymes catalyzing potentially useful flavonoid modification reactions.

scholarly journals Species and characteristics of volatile organic compounds emitted from an auto-repair painting workshop

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Y. Song ◽  
H. Chun
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Large Scale ◽  
Butyl Acetate ◽  
Small Scale ◽  
Indoor Environments ◽  
Voc Emissions ◽  
Volatile Organic ◽  
Before And After ◽  
Adsorption Systems

AbstractVolatile organic compounds (VOCs) are secondary pollutant precursors having adverse impacts on the environment and human health. Although VOC emissions, their sources, and impacts have been investigated, the focus has been on large-scale industrial sources or indoor environments; studies on relatively small-scale enterprises (e.g., auto-repair workshops) are lacking. Here, we performed field VOC measurements for an auto-repair painting facility in Korea and analyzed the characteristics of VOCs emitted from the main painting workshop (top coat). The total VOC concentration was 5069–8058 ppb, and 24–35 species were detected. The VOCs were mainly identified as butyl acetate, toluene, ethylbenzene, and xylene compounds. VOC characteristics differed depending on the paint type. Butyl acetate had the highest concentration in both water- and oil-based paints; however, its concentration and proportion were higher in the former (3256 ppb, 65.5%) than in the latter (2449 ppb, 31.1%). Comparing VOC concentration before and after passing through adsorption systems, concentrations of most VOCs were lower at the outlets than the inlets of the adsorption systems, but were found to be high at the outlets in some workshops. These results provide a theoretical basis for developing effective VOC control systems and managing VOC emissions from auto-repair painting workshops.

scholarly journals TreeMerge: a new method for improving the scalability of species tree estimation methods

2019 ◽  
Vol 35 (14) ◽  
pp. i417-i426 ◽  
Author(s):  
Erin K Molloy ◽  
Tandy Warnow
Keyword(s):  
Large Scale ◽  
Species Tree ◽  
New Method ◽  
Divide And Conquer ◽  
Supplementary Information ◽  
Estimation Methods ◽  
Running Time ◽  
Tree Estimation ◽  
Computationally Intensive ◽  
A Minor

Abstract Motivation At RECOMB-CG 2018, we presented NJMerge and showed that it could be used within a divide-and-conquer framework to scale computationally intensive methods for species tree estimation to larger datasets. However, NJMerge has two significant limitations: it can fail to return a tree and, when used within the proposed divide-and-conquer framework, has O(n5) running time for datasets with n species. Results Here we present a new method called ‘TreeMerge’ that improves on NJMerge in two ways: it is guaranteed to return a tree and it has dramatically faster running time within the same divide-and-conquer framework—only O(n2) time. We use a simulation study to evaluate TreeMerge in the context of multi-locus species tree estimation with two leading methods, ASTRAL-III and RAxML. We find that the divide-and-conquer framework using TreeMerge has a minor impact on species tree accuracy, dramatically reduces running time, and enables both ASTRAL-III and RAxML to complete on datasets (that they would otherwise fail on), when given 64 GB of memory and 48 h maximum running time. Thus, TreeMerge is a step toward a larger vision of enabling researchers with limited computational resources to perform large-scale species tree estimation, which we call Phylogenomics for All. Availability and implementation TreeMerge is publicly available on Github (http://github.com/ekmolloy/treemerge). Supplementary information Supplementary data are available at Bioinformatics online.

CLIL theory and empirical reality – Two sides of the same coin?

2017 ◽  
Vol 5 (1) ◽  
pp. 110-134 ◽  
Author(s):  
Dominik Rumlich
Keyword(s):  
Large Scale ◽  
Self Concept ◽  
Empirical Reality ◽  
Longitudinal Model ◽  
Composition Effects ◽  
Class Composition ◽  
Quasi Experimental ◽  
Individual Variables ◽  
Conducting Research ◽  
A Minor

This article summarizes the essential theoretical and empirical findings of a large-scale doctoral dissertation study on content and language integrated learning (CLIL) streams at German secondary schools (Gymnasium) with up to three content subjects taught in English (Rumlich, 2016). A theoretical account rooted in teaching English as a foreign language (EFL), language acquisition and educational psychology provides the basis for the development of a comprehensive longitudinal model of general EFL proficiency, which incorporates cognitive, affective-motivational, and further individual variables. In a second step, the model is used to estimate the effects of CLIL on general EFL proficiency, EFL self-concept and interest over a span of two school years (Year 6 to Year 8). The statistical evaluation of the quasi-experimental data from 1,000 learners finds large initial differences prior to CLIL due to selection, preparation, and class composition effects brought about by the implementation of CLIL within streams. After two years, the analyses found no CLIL-related benefits for general EFL proficiency or interest in EFL classes and solely a minor increase in EFL self-concept that might be attributable to CLIL. The results make a strong claim for comprehensive longitudinal model-based evaluations and the inclusion of selection, preparation, and class composition effects when conducting research on CLIL programmes in similar settings. The findings also suggest that not all language competences and affective-motivational dispositions might benefit from CLIL (the way it is currently taught in Germany) to the same extent.

scholarly journals Temperature-dependence of aerosol optical depth over the southeastern US

2016 ◽  
Author(s):  
Tero Mielonen ◽  
Anca Hienola ◽  
Thomas Kühn ◽  
Joonas Merikanto ◽  
Antti Lipponen ◽  
...  
Keyword(s):  
Temperature Dependence ◽  
Land Surface ◽  
Radiative Forcing ◽  
Climate Model ◽  
Biogenic Emissions ◽  
Anthropogenic Emissions ◽  
Model Estimate ◽  
Model Simulations ◽  
Southeastern Us ◽  
Biogenic Aerosols

Abstract. Previous studies have indicated that summer-time aerosol optical depths (AOD) over the southeastern US are dependent on temperature but the reason for this dependence and its radiative effects have so far been unclear. To quantify these effects we utilized AOD and land surface temperature (LST) products from the Advanced Along-Track Scanning Radiometer (AATSR) with observations of tropospheric nitrogen dioxide (NO2) column densities from the Ozone Monitoring Instrument (OMI). Furthermore, simulations of the global aerosol-climate model ECHAM-HAMMOZ have been used to identify the possible processes affecting aerosol loads and their dependence on temperature over the studied region. Our results showed that the level of AOD in the southeastern US is mainly governed by anthropogenic emissions but the observed temperature dependent behaviour is most likely originating from non-anthropogenic emissions. Model simulations indicated that biogenic emissions of volatile organic compounds (BVOC) can explain the observed temperature dependence of AOD. According to the remote sensing data sets, the non-anthropogenic contribution increases AOD by approximately 0.009 ± 0.018 K−1 while the modelled BVOC emissions increase AOD by 0.022 ± 0.002 K−1. Consequently, the regional direct radiative effect (DRE) of the non-anthropogenic AOD is −0.43 ± 0.88 W/m2/K and −0.17 ± 0.35 W/m2/K for clear- and all-sky conditions, respectively. The model estimate of the regional clear-sky DRE for biogenic aerosols is also in the same range: −0.86 ± 0.06 W/m2/K. These DRE values indicate significantly larger cooling than the values reported for other forested regions. Furthermore, the model simulations showed that biogenic emissions increased the number of biogenic aerosols with radius larger than 100 nm (N100, proxy for cloud condensation nuclei) by 28 % per one degree temperature increase. For the total N100, the corresponding increase was 4 % which implies that biogenic emissions could also have a small effect on indirect radiative forcing in this region.

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