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100 results for “NOx”
Weekly NOx aviation emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>Weekly NOx aviation emissions estimates for 2020 until 21/07/2020, modified by the country-specific impacts of COVID-19 lockdown. </p> <p>This repository holds the netcdf files for NOx emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020, with observation-based data up until the 5th of July and a fixed estimate thereafter. This is the weekly equivalent of the aviation file in <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for a shorter time period, although the version number is different since we have more information available and the normalisation process has been improved. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details on the methodology.</p>
Simulating δ15N of atmospheric NOx with the impacts from atmospheric processes
<p>A new emission dataset of NO<sub>x</sub>, which incorporates the <sup>15</sup>N isotope, was used to simulate the δ<sup>15</sup>N values in CAMQ (the Community Multiscale Air Quality Modeling System), in order to qualitatively analyze the changes in δ<sup>15</sup>N values, due to the disperse, mixing, and transport of the atmospheric NO<sub>x</sub> emitted from different sources, driven by atmospheric processes.</p>
Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
<p><span><span>The selective catalytic reduction (SCR) de</span><span>-</span><span>NO<sub>x</sub> </span><span>process in coal-fired power plants not only displays nonlinearity, large inertia, and time variation but also a lag in NO<sub>x</sub> analysis; </span><span>hence,</span><span> it is difficult to obtain an accurate model </span><span>that </span><span>can be used to control NH<sub>3</sub> injection </span><span>during changes in the </span><span>operating state. </span><span>In this work,</span><span> a novel dynamic inferential model with delay estimation was proposed for NO<sub>x</sub> emission prediction. First, k-nearest neighbour mutual information (knnMI) was used to estimate the time-delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square (mwKPLS) was</span><span> used</span><span> to improve the prediction ability, </span><span>and this was followed by verification using </span><span>benchmark dataset experiments. Finally, the delay-time difference (DTD) method and feedback correction strategy </span><span>were </span><span>proposed to deal with the time variation of the SCR de</span><span>-</span><span>NO<sub>x</sub> process.</span> <span>Through the analysis of the </span><span>experimental field data </span><span>in the</span> <span>steady state, </span><span>the variable</span><span> state and </span><span>the </span>NO<sub>x</sub> analyser blowback process<span>, the results proved that</span><span> this dynamic model has </span><span>high prediction accuracy</span><span> during</span><span> state changes and can </span><span>realize</span><span> advance prediction of the NO<sub>x</sub> emission. </span></span></p>
Global anthropogenic NOx emissions from 2019 to 2022 based on satellite NO2 observations and GEOS-Chem model
<p><a href="../api/records/10947114/draft/files/emissions.nc/content" target="_blank" rel="noopener noreferrer">emissions.nc</a><a href="10052904"> includes the monthly data of global anthropogenic nox emissions from 2019 to 2022, using the GEOS-Chem model combined with TROPOMI satellite observation data</a></p>
Global gridded NOx emissions using TROPOMI observations
<p>This dataset provides top-down global gridded emissions of NOx for the year 2022. It is constructed from retrievals of tropospheric vertical column densities of NO2 by the TROPOMI spaceborne instrument associated with winds and atmospheric composition data from ECMWF reanalyses, using an improved version of the flux-divergence method. The emissions are provided with a spatial resolution of 0.0625°×0.0625° and deliver a detailed overview of the distribution of emissions. <br>This dataset also provides two lists of point and diffuse sources, defined as clusters of at least 3 contiguous pixels above the value of 2 Pmolecules/cm²/h for annual averaged NOx emissions, calculated with the given monthly gridded maps. Point sources are defined as clusters comprising 3 to 9 pixels while diffuse sources are clusters with more than 10 pixels. Corrections have been applied from the previous version of the dataset; changes are generally minor, except for emissions at very high latitudes.</p>
AUB (AdBlue verbruik, Uren, en Brandstofverbruik): een robuuste schatting van NOx en NH3 uitstoot van mobiele werktuigen
<p>Tabellen bij rapport TNO 2021 R12305 AUB (AdBlue verbruik, Uren, en Brandstofverbruik): een robuuste schatting van NOx en NH3 uitstoot van mobiele werktuigen.</p>
A seasonal analysis of aerosol NO3- sources and NOx oxidation pathways in the Southern Ocean marine boundary layer
<p>This dataset includes coarse mode atmopsheric nitrate concentration and isotopic composition from the Southern Ocean marine boundary layer in summer (2018/19), winter (2019) and spring (2019). Environmental data (temperature, SLP and relative humidity) are also included.</p>
KORUS-AQ gridded O3, NOx, and CO observations created using ground station data
<p>We present gridded surface air quality datasets over South Korea for three key species – ozone (O<sub>3</sub>), carbon monoxide (CO), and nitrogen oxides (NO<sub>x</sub>) during the timeframe of the Korea–US Air Quality (KORUS–AQ) mission (May–June 2016). The tenth degree hourly averaged abundances are constructed from the 300+ air quality network sites using inverse distance weighting with simple declustering. Cross–comparing the interpolated fields against the site data that was used to create them reveals high prediction skill for O<sub>3</sub> (80%) throughout South Korea, and moderate skill (60%) for CO and NOx on average in densely observed regions after individual mean bias corrections. The gridded O<sub>3</sub> and CO interpolations predict the NASA DC–8 observations in the planetary boundary layer (PBL) with high skill (80%) in the Seoul Metropolitan Area (SMA) after subtracting the mean bias. DC–8 NOx observations were much less predictable on account of consistently negative vertical gradients within the PBL. Our gridded products capture the mean and variability of O<sub>3</sub> throughout South Korea, and of CO and surface NO<sub>x</sub> in most site–dense urban centres (SMA, Cheongju, Gwangju, Daegu, Changwon, and Busan).</p>
The influence of electric circuit parameters on NOx generation by transient spark discharge _ data
<p>dataset for</p> <p>The influence of electric circuit parameters on NOx generationby transient spark discharge</p> <p> </p> <p>Abstract</p> <p>Nitrogen fixation, production of NO and NO<sub>2</sub> from N<sub>2</sub> and O<sub>2</sub> in air, has been investigated with<br> transient spark self-pulsing DC discharges. NO production is boosted by the addition of capacitors<br> and an inductor to the electrical circuit which drives the discharge. The quantity of NO produced<br> per joule of electrical input energy is doubled, though the quantity of NO<sub>2</sub> produced drops. The<br> yield of NO is also increased because the modified circuit enables higher discharge currents to be<br> used. NO concentrations as high as 2000 ppm were obtained with input energy densities of around<br> 300 J per litre of input gas, whilst NO<sub>2</sub> concentrations were around 150 ppm. This simple<br> modification of the driving circuit may have potential for optimizing the plasma chemistry with<br> other input gas mixtures and for scaling up nitrogen fixation from air.</p>
Synergy between deep learning and numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution
<p>This study focused on the remarkable applicability of deep learning (DL) together with numerical modeling in estimating NO<sub>x</sub> emissions at a fine spatiotemporal resolution in the summer of 2017 over the contiguous United States (CONUS). We leveraged the partial convolutional neural network (PCNN) and the deep neural network (DNN) to impute gaps in the OMI tropospheric NO<sub>2</sub> column and estimate the daily complete surface NO<sub>2</sub> map at a spatial resolution of 10 km × 10 km, showing high capability with a strong correspondence (R: 0.92, IOA: 0.96, MAE: 1.43). We then used the Community Multi-scale Air Quality (CMAQ) model at 12 km grid spacing to conduct an inversion of NO<sub>x</sub> emissions that allowed us to promote a comprehensive understanding of the chemical evolution. Compared to the prior emissions, the inversion suggested 3.21 ± 3.34 times higher NO<sub>x</sub> emissions over CONUS, significantly mitigating the underestimation of surface NO<sub>2</sub> concentrations with the prior emissions. The results displayed the primary benefits of incorporating DL-estimated daily complete surface NO<sub>2</sub> map, which in turn greatly reduced bias (-1.53 ppb to 0.26 ppb) and enhanced daily variability with higher correspondence (0.84 to 0.92) and lower error (0.48 ppb to 0.10 ppb) over the CONUS. </p>
WRF_NOx_output
<p>WRF model outputs</p>
KORUS-AQ gridded O3, NOx, and CO observations created using ground station data
Open the record for dataset details and reuse information.
Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
Open the record for dataset details and reuse information.
Supporting data for "Direct observation of changing NOx lifetime in North American cities"
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Effects of Photocatalytic Coatings in reducing NOx pollutant concentrations (2018)
<p>This dataset contains the observations of air quality collected within one experimental campaign conducted in August 2018 at Lazzaretto campus of the University of Bologna to verify the effectiveness of photocatalytic coatings in reducing pollutant concentrations. The dataset also contains the meteorological data collected during non-synoptic periods, representing the ideal condition to assess the behavior of the local circulation and turbulence. This experimental field campaign was conducted under WP3 (“Planning and evaluation of PCS solutions”) of the iSCAPE project. Briefly, two parallel street canyons were instrumented for measurements of meteorology and turbulence variables at two height levels, while pollutant concentrations were monitored at ground level inside both canyons. After a first period of parallel contemporary measurements to verify the presence of intrinsic differences between the two canyons, one of the two canyons (Canyon A) was painted with photocatalytic coatings, while the other (Canyon B) was left untouched and used as reference canyon. This dataset is linked to the paper “The effect of photocatalytic coatings on NOx concentrations in real-world street canyons” by Brattich E. et al., which has been submitted for publication on “Science of the Total Environment” edited by Elsevier. If you use this dataset for your research or work purposes, we ask that you acknowledge us in the use of your data, which can be done by including a proper citation in any documents or publications using these data.</p>
FIGURES 17–19. Wings. 17. Otites grata. 18. O. nox n in Review of Otites Latreille (Diptera: Ulidiidae) from Israel with two new species and notes on biology and behavior
FIGURES 17–19. Wings. 17. Otites grata. 18. O. nox n. sp. 19. O. vitalyi n. sp. Abbreviations: BB—basal band, SBB—subbasal band, PB—pterostigmal band, SAB—subapical band, AB—apical band.
Brustiarius nox RB0369 NOVOGENE decontaminated FSCR
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Brustiarius nox RB0369 ARBOR SPAdes preassembly
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Brustiarius nox RB0369 ARBOR SPAdes preassembly
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Brustiarius nox RB0369 ARBOR decontaminated FSCR
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