Nitrogen oxide | EPFL Graph Search

Nitrogen oxide may refer to a binary compound of oxygen and nitrogen, or a mixture of such compounds: Nitric oxide (NO), nitrogen(II) oxide, or nitrogen monoxide Nitrogen dioxide (), nitrogen(IV) oxide Nitrogen trioxide (), or nitrate radical Nitrous oxide (), nitrogen(0,II) oxide Dinitrogen dioxide (), nitrogen(II) oxide dimer Dinitrogen trioxide (), nitrogen(II,IV) oxide Dinitrogen tetroxide (), nitrogen(IV) oxide dimer Dinitrogen pentoxide (), nitrogen(V) oxide, or nitronium nitrate Nitrosyl azide (), nitrogen(−I,0,I,II) oxide Nitryl azide () Oxatetrazole () Trinitramide ( or ), nitrogen(0,IV) oxide Nitrosonium ( or ) Nitronium ( or ) In atmospheric chemistry: (or NOx) refers to the sum of NO and . (or NOy) refers to the sum of and all oxidized atmospheric odd-nitrogen species (e.g. the sum of , , , etc.) (or NOz) = − Mixed Oxides of Nitrogen ("MON"): solutions of nitric oxide in dinitrogen tetroxide/nitrogen dioxide. Image:Nitric-oxide-3D-vdW.png|{{center|'''[[Nitric oxide]]''', NO}} Image:Nitrogen-dioxide-3D-vdW.png|{{center|'''[[Nitrogen dioxide]]''', {{chem2|NO2}}}} Image:Nitrous-oxide-3D-vdW.png|{{center|'''[[Nitrous oxide]]''', {{chem2|N2O}}}} Image:Dinitrogen-trioxide-3D-vdW.png|{{center|'''[[Dinitrogen trioxide]]''', {{chem2|N2O3}}}} Image:Dinitrogen-tetroxide-3D-vdW.png|{{center|'''[[Dinitrogen tetroxide]]''', {{chem2|N2O4}}}} Image:Dinitrogen-pentoxide-3D-vdW.png|{{center|'''[[Dinitrogen pentoxide]]''', {{chem2|N2O5}}}} Image:Trinitramide-3D-spacefill.png|{{center|'''[[Trinitramide]]''', {{chem2|N4O6}}}} Due to relatively weak N–O bonding, all nitrogen oxides are unstable with respect to and , which is the principle behind the catalytic converter and prevents the combustion of the atmosphere.

Statistical methods for accounting and understanding ozone trends derived from observations

Eirini Boleti

Emissions of ozone precursors have been regulated in Europe since around 1990 with air quality control measures, which resulted in reductions of nitrogen oxides and volatile organic compounds concentrations. In order to understand how these measures have affected tropospheric ozone, it is important to investigate its long-term temporal evolution in different types of environments and various geographic regions. Uncertainties in ozone long-term trends are associated to variations originating from meteorological influence on ozone. Also, ozone temporal evolution can vary significantly among different regions and types of environment. In this PhD thesis we used sophisticated statistical tools, and developed robust statistical approaches to study long-term trends of tropospheric ozone that reflect emissions reductions. First, we focus on a meteorological adjustment of ozone observations in order to derive long-term trends with lower uncertainties compared to common practices. In addition, a classification scheme for stations in Europe is needed to interpret response of ozone in site groups with similar spatio-temporal characteristics. A detailed long-term trend analysis was performed based on decomposition of the mean ozone observations in the time domain. The different time-dependent variations of ozone were extracted, namely the long-term trend, seasonal and short-term variability. This allows subtraction of the meteorologically driven seasonal variation from the observations and estimation of long-term trends on de-seasonalized concentrations. In addition, ozone peak concentrations were investigated using a localized regression approach, which corrects for meteorological influence. The meteorological adjustment of the mean and peak ozone allows estimation of long-term trends with lower uncertainty. A site grouping in Europe was developed using the long-term and seasonal variations of ozone. The implemented clustering approach based on the long-term variation resulted in a site type classification while a geographical classification was achieved on the seasonal variation. We observed that, despite the implementation of regulations, mean ozone has been increasing in most of the sites until mid-2000s, although, afterwards, a decline or a leveling off was observed. The point when the trend changes from increasing to decreasing depends on the site type; the closer a site locates to emission sources the later the change occurred. Also, it was concluded that with time urban and rural environments become more similar in terms of ozone concentrations. On the other hand, peak ozone has been reducing in most stations, while in sites close to emissions it increased until mid-2000s when it started to level off. The influence of air pollutants hemispheric transport is depicted in remote sites, where ozone increased until beginning of 2000s and decreased afterwards. A two-dimensional classification scheme, reflecting site type and region, has showed that mainly the site type influences ozone long-term trends, while the location is more important for temporal changes in ozone inter-annual cycle and relationship to temperature.

EPFL2020

Comparison of two carbon-nitrogen regulatory models calibrated with mesocosm data

Shubham Krishna

Marine phytoplankton can regulate their stoichiometric composition in response to variations in the availability of nutrients, light and the pH of seawater. Varying elemental composition of photoautotrophs affects several important ecological and biogeochemical processes, e.g., primary and export production, nutrient cycling, calcification, and grazing. Here we compare two plankton ecosystem models that consider regulatory mechanisms of cellular carbon and nitrogen, driving the physiological acclimation of photoautotrophs. The Carbon:Nitrogen Regulated Ecosystem Model (CN-REcoM) and the optimality-based model (OBM) differ in their representation of phytoplankton dynamics, i.e. nutrient acquisition, synthesis of chlorophyll a, and growth. All other model compartments (zooplankton, detritus, dissolved inorganic and organic matter) and processes (grazing, aggregation, remineralisation) remain identical in both models. We assess the skills of the two models against data from an ocean acidification mesocosm experiment with three CO2 treatments. Neither model accounts for any carbon dioxide (CO2) effects explicitly. Instead, we assimilate data of the different CO2 treatments separately into the models. Thereby we aim at identifying optimal model parameter values that might correlate with differences in CO2 conditions. For the OBM, optimal parameter estimates of Q(min) (subsistence N:C ratio) and V-C(0) (maximum potential photosynthesis rate of photoautotrophs) turned out to be higher for mesocosms exposed to high CO2 compared to those with low CO2 concentrations. By contrast, a similar correlation is not observed for the CN-REcoM. A possible physiological interpretation of higher estimates of Q(min), and V-C(0) according to the OBM is that phytoplankton may experience environmental stress under more acidic conditions, and hence must invest more energy/resources for maintaining basic cellular functions. Our data assimilation reveals that the parameters of the OBM are better constrained by the data than those of the CN-REcoM. Furthermore, the OBM is better able than CN-REcoM to reproduce data that were not used for parameter optimization.

2019