Atmospheric dispersion modeling near a roadway under calm meteorological conditions

Atmospheric pollutant dispersion near sources is typically simulated by Gaussian models because of their efficient compromise between reasonable accuracy and manageable com- putational time. However, the standard Gaussian dispersion formula applies downwind of a source under advective conditions with a well-defined wind direction and cannot calculate air pollutant concentrations under calm conditions with fluctuating wind direction and/or upwind of the emission source. Attempts have been made to address atmospheric disper- sion under such conditions. This work evaluates the performance of standard and modified Gaussian plume models using measurements of NO2, PM10, PM2.5, five inorganic ions and seven metals conducted near a freeway in Grenoble, France, during 11-27 September 2011. The formulation for calm conditions significantly improves model performance. However, it appears that atmospheric dispersion due to vehicle-induced turbulence is still underestimated. Furthermore, model performance is poor for particulate species unless road dust resuspension by traffic is explicitly taken into account

Intercomparison of three modeling approaches for traffic-related road dust resuspension using two experimental data

Two observational campaigns were conducted, one in the Grenoble area (South Eastern France), for the MOCoPo project, near an urban freeway in 2011 and the other one in a Paris suburb, forthe TrafiPollu project, on a major surface street in 2014. PM10 concentrations were measured by Air Rhône-Alpes during the last 10 days of September 2011 for MOCoPo and by Airparif during 3 months from April to June 2014 for TrafiPollu. It has been shown that abrasion and resuspension processes represent a significant part of the total primary PM10 emissions of road traffic. Hereby, resuspended emissions originating from the road are estimated with several approaches and compared to PM10 measurements. We consider two different models available in the literature: HERMES (Pay et al., 2010) and NORTRIP (Denby et al., 2013), which differ in terms of formulation. We also apply an empirical method developed by Thorpe et al. (2007), based on near-road and background pollutant observations. The results vary depending on the traffic conditions and the modeling approach. In all cases, the resuspension emissions simulated are high enough to be considered in air quality modeling (ranging from 9 to 150% of the exhaust emissions). Those resuspension models were combined with atmospheric dispersion models to estimate near-road concentrations. We used a Gaussian line-source model for the Grenoble urban freeway and a street-canyon model (MUNICH) for the Paris suburban boulevard. The contribution of resuspension to traffic-related concentrations is hidden by a strong background contribution, which prevents us from concluding in terms of model performance. Nevertheless, a comparison with another dataset obtained near an urban freeway in Paris suggests that vehicle speed should be taken into account when estimating PM10 resuspension emissions.Deux campagnes d'observation ont été menées, l'une dans la région grenobloise, pour le projet MOCoPo, près d'une autoroute urbaine en 2011, et l'autre dans une banlieue parisienne, pour le projet TrafiPollu, sur une grande rue en surface en 2014. Les concentrations de PM10 ont été mesurées par Air Rhône-Alpes pendant les 10 derniers jours de septembre 2011 pour MOCoPo et par Airparif pendant 3 mois d'avril à juin 2014 pour TrafiPollu. Il a été démontré que les procédés d'abrasion et de remise en suspension représentent une part importante des émissions primaires totales de PM10 du trafic routier. Ainsi, les émissions remises en suspension provenant de la route sont estimées selon plusieurs approches et comparées aux mesures des PM10. Nous considérons deux modèles différents disponibles dans la littérature : HERMES (Pay et al., 2010) et NORTRIP (Denby et al., 2013), qui diffèrent en termes de formulation. Nous appliquons également une méthode empirique développée par Thorpe et al (2007), basée sur des observations de polluants de fond et de proximité de la route. Les résultats varient en fonction des conditions de circulation et de l'approche de modélisation. Dans tous les cas, les émissions de resuspension simulées sont suffisamment élevées pour être prises en compte dans la modélisation de la qualité de l'air (allant de 9 à 150 % des émissions d'échappement). Ces modèles de resuspension ont été combinés à des modèles de dispersion atmosphérique pour estimer les concentrations près des routes. Nous avons utilisé un modèle gaussien de source linéaire pour l'autoroute urbaine de Grenoble et un modèle rue-canyon (MUNICH) pour le boulevard de la banlieue parisienne. La contribution de la remise en suspension aux concentrations liées au trafic est masquée par une forte contribution de fond, ce qui nous empêche de conclure en termes de performance du modèle. Néanmoins, une comparaison avec un autre ensemble de données obtenu près d'une autoroute urbaine à Paris suggère que le véhicule devrait être prise en compte lors de l'estimation des émissions de resuspension des PM10

Characterising an intense PM pollution episode in March 2015 in France from multi-site approach and near real time data : Climatology, variabilities, geographical origins and model evaluation

International audienceDuring March 2015, a severe and large-scale particulate matter (PM) pollution episode occurred in France. Measurements in near real-time of the major chemical composition at four different urban background sites across the country (Paris, Creil, Metz and Lyon) allowed the investigation of spatiotemporal variabilities during this episode. A climatology approach showed that all sites experienced clear unusual rain shortage, a pattern that is also found on a longer timescale, highlighting the role of synoptic conditions over Wester-Europe. This episode is characterized by a strong predominance of secondary pollution, and more particularly of ammonium nitrate, which accounted for more than 50% of submicron aerosols at all sites during the most intense period of the episode. Pollution advection is illustrated by similar variabilities in Paris and Creil (distant of around 100 km), as well as trajectory analyses applied on nitrate and sulphate. Local sources, especially wood burning, are however found to contribute to local/ regional sub-episodes, notably in Metz. Finally, simulated concentrations from Chemistry-Transport model CHIMERE were compared to observed ones. Results highlighted different patterns depending on the chemical components and the measuring site, reinforcing the need of such exercises over other pollution episodes and sites

La campagne Passy-2015 : dynamique atmosphérique et qualité de l’air dans la vallée de l’Arve

International audienceWintertime anticyclonic conditions, associated with clear sky and cold nights, trigger the formation of persistent layers of stable air over the ground. In an urban area, these persistent layers lead to poor air quality, especially when the terrain is mountainous. This is particularly the case in the Arve River Valley near the city of Passy, located 20 km downstream of Chamonix-Mont-Blanc, where air quality stands among the poorest ones in France.Beyond the monitoring of air quality, as performed by the Auvergne-Rhône-Alpes air quality agency or within the scientific project DECOMBIO led by the Institute for Geosciences and the Environment (IGE), knowledge of the atmospheric dynamics at the valley scale should be gained to understand how pollutants are dispersed. This is the motivation of the Passy project, which started in 2014. It relies on the Passy-2015 field experiment, whereof presentation, along with the discussion of a few results, is the purpose of the present paper. The objective of this field experiment is to document the atmospheric dynamics in the Arve River Valley during wintertime pollution episodes.The work conducted during the Passy project and the analysis of the Passy-2015 field experiment will benefit from a several-year long collaboration among the different partners. The knowledge thus gained will contribute to refine weather forecast and air quality prediction in the Arve River Valley and, more generally, in mountain urban areas under stable conditions. From an operational perspective, our goal is to improve our ability to forecast critical events such as low temperatures, ice and fog formation, pollution events or locations subject to high pollutant concentration.Les conditions anticycloniques hivernales (ciel clair, nuits froides) conduisent à la formation de couches stables persistantes qui favorisent les épisodes de pollution, particulièrement en terrain montagneux. La vallée de l’Arve est très sensible à ce phénomène, en particulier près de la ville de Passy (Haute-Savoie), située à 20 kilomètres en aval de Chamonix-Mont-Blanc, où la qualité de l’air est l’une des moins bonnes de France.Au-delà du suivi de la qualité de l’air, tel que réalisé par Atmo Auvergne-Rhône-Alpes ou par le projet DECOMBIO piloté par l’Institut des Géosciences et de l’Environnement (IGE), il est primordial d’améliorer la connaissance de la dynamique atmosphérique à l’échelle de la vallée en conditions stables pour mieux comprendre comment, couplée au cycle et à la géographie des émissions, elle pilote la dispersion des polluants. C’est la motivation du projet Passy, initié en 2014. Ce projet s’appuie sur les observations de la campagne Passy-2015, présentées dans cet article avec quelques premiers résultats. L’objectif général de cette campagne est de documenter la dynamique atmosphérique au sein de la vallée de l’Arve lors des épisodes de pollution hivernale.Les travaux menés dans le cadre du projet et de l’analyse des données de la campagne s’inscrivent au sein d’une collaboration sur plusieurs années entre les différents partenaires. Ils contribueront à affiner la prévision du temps et de la qualité de l’air dans ce type de vallée, et plus généralement en conditions stables. Il s’agit en particulier d’améliorer la capacité à prévoir des phénomènes critiques, comme les températures minimales, le verglas, le brouillard, les évènements de pollution ou encore les zones de pollution intense