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41 results for “NO3”
Experimental datasets from Newland et al. (2021, ACP, NO3 chemistry of wildfire emissions: a kinetic study of the gas-phase reactions of furans with the NO3 radical)
<p>This folder contains raw FTIR output in .csv format for the furan+NO3 experiments in Newland et al. (2021). The dates (and so relevant VOCs) for each experiment can be found in Table S1.</p> <p>FTIR reference spectra for the furans are also provided. All spectra were recorded at ~ 3 ppmv.</p>
Experimental datasets from Newland et al. (2021, ACP, NO3 chemistry of wildfire emissions: a kinetic study of the gas-phase reactions of furans with the NO3 radical)
<p>This folder contains response - time profiles obtained using the ANIR program to interpret the raw FTIR output (available at 10.5281/zenodo.5721518) for the furan+NO3 experiments in Newland et al. (2021). The dates (and so relevant VOCs) for each experiment can be found in Table S1.</p> <p>The first column of each file is the response-time profile. The other three columns are residuals calculated by the ANIR program when fitting the reference spectra to the experiment spectra.</p>
Nitrate uptake estimates phytoplankton incorporation of dissolved NO3- at selected depths from CCE LTER process cruises in the California Current System, 2011 – 2016 (ongoing).
Nitrate uptake samples (1.1-L) of seawater are taken each day shortly before noon on the CTD rosette up-cast during the CCE Process cruises (since 2011, ongoing). Approximate nitrate concentrations are determined at sea and the samples are spiked with 15NO3- at a concentration equivalent to ~10% ambient nitrate. Samples (one sample per depth) are incubated for 24 hours in polycarbonate bottles held at natural light and temperature conditions on the in situ array. After recovery samples are filtered onto pre-combusted GF/F filters, which are analyzed for particulate nitrogen and δ15N by mass spectrometry in the Scripps Analytical Facility. Accurate nitrate concentrations are determined after the cruise from frozen and filtered samples analyzed by autoanalyzer (see separate Dissolved Inorganic Nutrients dataset by Ralf Goericke). Nitrate uptake is calculated using the equations in Dugdale & Wilkerson (1986). In surface waters where nitrate concentrations are often quite low, we spike with a minimum of 10 nmol L-1 15NO3-. Since this can lead to a bias (high) in calculated nitrate uptake, when post-cruise nitrate analyses showed that our spike concentration was greater than 10% of ambient nitrate, we multiplied by the uptake calculated from the equations of Dugdale & Wilkerson (1986) by (ambient NO3-)/(ambient NO3- + added 15NO3-). Primary production was simultaneously estimated from 14C uptake in triplicate 250-mL bottles (plus a dark bottle) incubated similarly on the in situ array (see separate Primary Production – Particulate dataset by Ralf Goericke). We calculated f-ratios by assuming that phytoplankton uptake occurred at Redfield carbon:nitrogen ratios of 106:16 (mol:mol). To eliminate the impact of phytoplankton that may be engaged in luxury nitrate uptake in the deep euphotic zone, we also report conservative nitrate uptake, which is the minimum of nitrate uptake or primary production divided by the Redfield C:N ratio. This value thus sets a maximum for nitrate
TROPESS Chemical Reanalysis Aerosol NO3 6-Hourly 3-dimensional Product V1 (TRPSCRAERNO36H3D) at GES DISC
The TROPESS Chemical Reanalysis Surface Aerosol NO3 6-Hourly 3-dimensional Product contains the volume mixing rations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.
TROPESS Chemical Reanalysis Aerosol NO3 Monthly 3-dimensional Product V1 (TRPSCRAERNO3M3D) at GES DISC
The TROPESS Chemical Reanalysis Surface Aerosol NO3 Monthly 3-dimensional Product contains the volume mixing rations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.
TROPESS Chemical Reanalysis Surface Aerosol NO3 2-Hourly 2-dimensional Product V1 (TRPSCRAERNO32H2D) at GES DISC
The TROPESS Chemical Reanalysis Surface Aerosol NO3 2-Hourly 2-dimensional Product contains surface concentrations of nitrate aerosols. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.
Col d'Olen, water chemistry (N-NO3) 2008-2019
<p> </p> <p>Dataset provides information about soil temperature, soil C and N dynamics at different sites in the alpine tundra, along an elevational gradient from 2500 to 2900 m asl during the period 2005-2018. Moreover, in ordrer to evaluate how topsoil can influence the quality of surface water, lake water was sampled monthly at the same time that soil was sampled. The N-NO3 concentration is measured from 2008 to 2019 in water samples collected in triplicate in two lakes (Cimalegna and Bowditch). The ongoing studies aimed at investigating how the meteorological variables affect the biogeochemistry of seasonally snow covered areas, with a special focus on the effect of the snow cover duration on soil and water C and N dynamics during the growing season.</p>
Col d'Olen, snow chemistry (N-NO3) 2009-2019
<p>Dataset provides information about soil temperature, soil C and N dynamics at different sites in the alpine tundra, along an elevational gradient from 2500 to 2900 m asl during the period 2005-2018. Moreover, one snow profile was opened every year from 2009 to 2019 before snowpack isothermal condition (to avoid losses via ionic pulse). Nitrate stored in snowpack is rilevant on yearly ion balance.</p>
Col d'Olen, soil chemistry (extractable N-NO3) 2008-2019
<p>Dataset provides information about soil temperature, soil C and N dynamics at different sites in the alpine tundra, along an elevational gradient from 2500 to 2900 m asl during the period 2005-2018. The soil was sampled in triplicate in each the sites and the N-NO3 concentration is measured in soil K2SO4 extracts. The ongoing studies aimed at investigating how the meteorological variables affect the biogeochemistry of seasonally snow covered areas, with a special focus on the effect of the snow cover duration on soil and water C and N dynamics during the growing season.</p>
Long-term trends of ambient nitrate (NO3-) concentrations across China based on ensemble machine-learning models
<p>The data is the monthly NO3- concentrations across China during 2005-2015. These data was obtained using a novel ensemble model combining random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) algorithms based on satellite data, assimilated meteorology, and other geographical covariates.</p> <p>In the datasets, XX-YY denote the XX month in YY year.<br> For instance, January-05 denotes the January in 2005.<br> NaN in the data denote the missing values.</p>
Clonenagh - No3
Text Taken from Con Manning 'Cross-Slabs from Clonenagh Co. Laois' The corner of a larger slab decorated on one face. A double-line border along its two original edges forms an obtuse angle to avoid the right-angled corner of the stone, where part of the face was missing prior to carving. Two Greek Crosses within circles in false relief and with tiny holes at their centres. One is incomplete. The surface is also decorated with spirals.  Source: Objaverse 1.0 / Sketchfab
Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019 v1
The Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of the chemical concentrations at a hyper resolution (50m x 50m grid cells) in urban areas and at a high resolution (1km x 1km grid cells) in non-urban areas for the years 2000 to 2019. Particulate matter with an aerodynamic diameter less than 2.5 �m (PM2.5) increases mortality and morbidity. PM2.5 is composed of a mixture of chemical components that vary across space and time. Due to limited hyperlocal data availability, less is known about health risks of PM2.5 components, their U.S.-wide exposure disparities, or which species are driving the biggest intra-urban changes in PM2.5 mass. The national super-learned models were developed across the U.S. for hyperlocal estimation of annual mean elemental carbon, ammonium, nitrate, organic carbon, and sulfate concentrations across 3,535 urban areas at a 50m spatial resolution, and at a 1km resolution for non-urban areas from 2000 to 2019. Using Machine-Learning models (ML), combined with either a Generalized Additive Model (GAM) Ensemble Geographically-Weighted-Averaging (GAM-ENWA) or Super-Learning (SL) and approximately 82 billion predictions across 20 years, hyperlocal super-learned PM2.5 components are now available for further research. The overall R-squared values of 10-fold cross validated models ranged from 0.910 to 0.970 on the training sets for these components, while on the test sets the R-squared values ranged from 0.860 to 0.960. Remarkable spatiotemporal intra-urban and inter-urban variabilities were found in PM2.5 components. The Coordinate Reference System (CRS) for predictions is the World Geodetic System 1984 (WGS84) and the Units for the PM2.5 Components are �g/m^3. The data are provided in RDS tabular format, a file format native to the R programming language, but can also be opened by other languages such as Python.
A systemic view of coordinated root responses to NO3- heterogeneous environment in Arabidopsis
GEO Series GSE22966. Arabidopsis thaliana. 36 samples. Type: Expression profiling by array.
High resolution NO3 response of Arabidopsis Roots
GEO Series GSE20044. Arabidopsis thaliana. 26 samples. Type: Expression profiling by array.
Long-term trends of ambient nitrate (NO3-) concentrations across China based on ensemble machine-learning models
<p>The monthly NO3- concentrations across China during 2005-2015</p>
Regulation of nitrogen acquisition (NO3-, NH4+ et N2) by nitrogen status
GEO Series GSE9818. Sinorhizobium meliloti; Medicago sativa; Medicago truncatula. 26 samples. Type: Expression profiling by array.
Transcriptional characterization of maize roots in response to nitrate (NO3-), Water-Extractable Humic Substances (WEHS) and NO3-+WEHS.
GEO Series GSE103184. Zea mays. 15 samples. Type: Expression profiling by array.
Nutritional control of plant development: molecular analysis of the NO3- response pathway in Arabidopsis roots.
GEO Series GSE6155. Arabidopsis thaliana. 7 samples. Type: Expression profiling by array.
nrt2.4-NRT2.4 NO3 transport mutant.
GEO Series GSE10786. Arabidopsis thaliana. 4 samples. Type: Expression profiling by array.
NRT2.4 NO3 transport mutant
GEO Series GSE7933. Arabidopsis thaliana. 4 samples. Type: Expression profiling by array.
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