Attribution of ground-level ozone to anthropogenic and natural sources of nitrogen oxides and reactive carbon in a global chemical transport model

We perform a source attribution for tropospheric and ground-level ozone using a novel technique that accounts separately for the contributions of the two chemically distinct emitted precursors (reactive carbon and oxides of nitrogen) to the chemical production of ozone in the troposphere. By tagging anthropogenic emissions of these precursors according to the geographical region from which they are emitted, we determine source-receptor relationships for ground-level ozone. Our methodology reproduces earlier results obtained via other techniques for ozone source attribution, and it also delivers additional information about the modelled processes responsible for the intercontinental transport of ozone, which is especially strong during the spring months. The current generation of chemical transport models used to support international negotiations aimed at reducing the intercontinental transport of ozone shows especially strong inter-model differences in simulated springtime ozone. Current models also simulate a large range of different responses of surface ozone to methane, which is one of the major precursors of ground-level ozone. Using our novel source attribution technique, we show that emissions of NOx (oxides of nitrogen) from international shipping over the high seas play a disproportionately strong role in our model system regarding the hemispheric-scale response of surface ozone to changes in methane, as well as to the springtime maximum in intercontinental transport of ozone and its precursors. We recommend a renewed focus on the improvement of the representation of the chemistry of ship NOx emissions in current-generation models. We demonstrate the utility of ozone source attribution as a powerful model diagnostic tool and recommend that similar source attribution techniques become a standard part of future model intercomparison studies

Our initial experience with ventriculo-epiplooic shunt in treatment of hydrocephalus in two centers

Introduction Hydrocephalus represents impairment in cerebrospinal fluid (CSF) dynamics. If the treatment of hydrocephalus is considered difficult, the repeated revisions of ventriculo-peritoneal (VP) shunts are even more challenging. Objective The aim of this article is to evaluate the efficiency of ventriculo-epiplooic (VEp) shunt as a feasible alternative in hydrocephalic patients. Material and methods A technical modification regarding the insertion of peritoneal catheter was imagined: midline laparotomy 8–10cm long was performed in order to open the peritoneal cavity; the great omentum was dissected between its two layers; we placed the distal end of the catheter between the two epiplooic layers; a fenestration of 4cm in diameter into the visceral layer was also performed. A retrospective study of medical records of 15 consecutive patients with hydrocephalus treated with VEp shunt is also presented. Results Between 2008 and 2014 we performed VEp shunt in 15 patients: 5 with congenital hydrocephalus, 8 with secondary hydrocephalus and 2 with normal pressure hydrocephalus. There were 7 men and 8 women. VEp shunt was performed in 13 patients with multiple distal shunt failures and in 2 patients, with history of abdominal surgery, as de novo extracranial drainage procedure. The outcome was favorable in all cases, with no significant postoperative complications. Conclusions VEp shunt is a new, safe and efficient surgical technique for the treatment of hydrocephalus. VEp shunt is indicated in patients with history of recurrent distal shunt failures, and in patients with history of open abdominal surgery and high risk for developing abdominal complications

Modeling of physico-chemical properties of atmospheric aerosols at high altitude

Non disponible.Aerosol particles are ubiquitous in the Earth’s atmosphere. Although a minor constituent of the atmosphere, the aerosol particles are linked to visibility reduction, adverse health effects and heat balance of the Earth. The secondary aerosols which are formed in the atmosphere from the gaseous phase : precursor gases become particles by nucleation and condensation (Seinfeld and Pandis, 1998) represents the largest source in a number concentration of atmospheric particles. The chemical reactions can play an important role by turning high volatility gases into species with low vapor pressure and thus high saturation ratio, i.e. creating favorable conditions for particulate matter formation. In this work the CHIMERE chemical transport model is used to ameliorate our understanding of the governing processes for aerosol formation and to investigate its capability to reproduce the mass and number concentrations and temporal evolution of the aerosols particles at high altitudes (as for example Puy de Dome research station), and in particular, evaluate its capacity to simulate the formation of new particles due to nucleation. For the studied cases it was investigated the impact of : a fine resolution topographical database on the accuracy of simulation of dynamical parameters at high altitude, of the use of different emissions databases in the accuracy of gas-phase and aerosol concentration predictions, what is the most adequate nucleation parameterization scheme for simulating new particle formation at high altitude and what is the influence of the choice of the primary particle size distribution on the prediction of new particle formation. Also the ability of the different theories to reproduce the occurrence or lack of a nucleation event is evaluated

Modélisation des propriétés physico-chimiques des aérosols atmosphériques à haute altitude

Aerosol particles are ubiquitous in the Earth’s atmosphere. Although a minor constituent of the atmosphere, the aerosol particles are linked to visibility reduction, adverse health effects and heat balance of the Earth. The secondary aerosols which are formed in the atmosphere from the gaseous phase : precursor gases become particles by nucleation and condensation (Seinfeld and Pandis, 1998) represents the largest source in a number concentration of atmospheric particles. The chemical reactions can play an important role by turning high volatility gases into species with low vapor pressure and thus high saturation ratio, i.e. creating favorable conditions for particulate matter formation. In this work the CHIMERE chemical transport model is used to ameliorate our understanding of the governing processes for aerosol formation and to investigate its capability to reproduce the mass and number concentrations and temporal evolution of the aerosols particles at high altitudes (as for example Puy de Dome research station), and in particular, evaluate its capacity to simulate the formation of new particles due to nucleation. For the studied cases it was investigated the impact of : a fine resolution topographical database on the accuracy of simulation of dynamical parameters at high altitude, of the use of different emissions databases in the accuracy of gas-phase and aerosol concentration predictions, what is the most adequate nucleation parameterization scheme for simulating new particle formation at high altitude and what is the influence of the choice of the primary particle size distribution on the prediction of new particle formation. Also the ability of the different theories to reproduce the occurrence or lack of a nucleation event is evaluated.Non disponible

Modélisation des propriétés physico-chimiques des aérosols atmosphériques à haute altitude

Non disponible.Aerosol particles are ubiquitous in the Earth s atmosphere. Although a minor constituent of the atmosphere, the aerosol particles are linked to visibility reduction, adverse health effects and heat balance of the Earth. The secondary aerosols which are formed in the atmosphere from the gaseous phase : precursor gases become particles by nucleation and condensation (Seinfeld and Pandis, 1998) represents the largest source in a number concentration of atmospheric particles. The chemical reactions can play an important role by turning high volatility gases into species with low vapor pressure and thus high saturation ratio, i.e. creating favorable conditions for particulate matter formation. In this work the CHIMERE chemical transport model is used to ameliorate our understanding of the governing processes for aerosol formation and to investigate its capability to reproduce the mass and number concentrations and temporal evolution of the aerosols particles at high altitudes (as for example Puy de Dome research station), and in particular, evaluate its capacity to simulate the formation of new particles due to nucleation. For the studied cases it was investigated the impact of : a fine resolution topographical database on the accuracy of simulation of dynamical parameters at high altitude, of the use of different emissions databases in the accuracy of gas-phase and aerosol concentration predictions, what is the most adequate nucleation parameterization scheme for simulating new particle formation at high altitude and what is the influence of the choice of the primary particle size distribution on the prediction of new particle formation. Also the ability of the different theories to reproduce the occurrence or lack of a nucleation event is evaluated.CLERMONT FD-Bib.électronique (631139902) / SudocSudocFranceF

Representing chemical history for ozone time-series predictions - a method development study for deep learning models

Machine learning techniques like deep learning gained enormous momentum in recent years. This was mainly caused by the success story of the main drivers like image and speech recognition, video prediction and autonomous driving, to name just a few.Air pollutant forecasting models are an example, where earth system scientists start picking up deep learning models to enhance the forecast quality of time series. Almost all previous air pollution forecasts with machine learning rely solely on analysing temporal features in the observed time series of the target compound(s) and additional variables describing precursor concentrations and meteorological conditions. These studies, therefore, neglect the 'chemical history' of air masses, i.e. the fact that air pollutant concentrations at a given observation site are a result of emission and sink processes, mixing and chemical transformations along the transport pathways of air.This study develops a concept of how such factors can be represented in the recently published deep learning model IntelliO3. The concept is demonstrated with numerical model data from the WRF-Chem model because the gridded model data provides an internally consistent dataset with complete spatial coverage and no missing values.Furthermore, using model data allows for attributing changes of the forecasting performance to specific conceptual aspects. For example, we use the 8 wind sectors (N, NE, E, SE, etc.) and circles with predefined radii around our target locations to aggregate meteorological and chemical data from the intersections. Afterwards, we feed this aggregated data into a deep neural network while using the ozone concentration of the central point's next timesteps as targets. By analysing the change of forecast quality when moving from 4-dimensional (x, y, z, t) to 3-dimensional (x, y, t or r, &#966;, t) sectors and thinning out the underlying model data, we can deliver first estimates of expected performance gains or losses when applying our concept to station based surface observations in future studies.</p

Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework

Tropospheric ozone is a secondary air pollutant that is harmful to living beings and crops. Predicting ozone concentrations at specific locations is thus important to initiate protection measures, i.e. emission reductions or warnings to the population. Ozone levels at specific locations result from emission and sink processes, mixing and chemical transformation along an air parcel's trajectory. Current ozone forecasting systems generally rely on computationally expensive chemistry transport models (CTMs). However, recently several studies have demonstrated the potential of deep learning for this task. While a few of these studies were trained on gridded model data, most efforts focus on forecasting time series from individual measurement locations. In this study, we present a hybrid approach which is based on time-series forecasting (up to 4 d) but uses spatially aggregated meteorological and chemical data from upstream wind sectors to represent some aspects of the chemical history of air parcels arriving at the measurement location. To demonstrate the value of this additional information, we extracted pseudo-observation data for Germany from a CTM to avoid extra complications with irregularly spaced and missing data. However, our method can be extended so that it can be applied to observational time series. Using one upstream sector alone improves the forecasts by 10 % during all 4 d, while the use of three sectors improves the mean squared error (MSE) skill score by 14 % during the first 2 d of the prediction but depends on the upstream wind direction. Our method shows its best performance in the northern half of Germany for the first 2 prediction days. Based on the data's seasonality and simulation period, we shed some light on our models' open challenges with (i) spatial structures in terms of decreasing skill scores from the northern German plain to the mountainous south and (ii) concept drifts related to an unusually cold winter season. Here we expect that the inclusion of explainable artificial intelligence methods could reveal additional insights in future versions of our model