Trace gas/aerosol boundary concentrations and their impacts on continental-scale AQMEII modeling domains

Copyright 2011 Elsevier B.V., All rights reserved.Over twenty modeling groups are participating in the Air Quality Model Evaluation International Initiative (AQMEII) in which a variety of mesoscale photochemical and aerosol air quality modeling systems are being applied to continental-scale domains in North America and Europe for 2006 full-year simulations for model inter-comparisons and evaluations. To better understand the reasons for differences in model results among these participating groups, each group was asked to use the same source of emissions and boundary concentration data for their simulations. This paper describes the development and application of the boundary concentration data for this AQMEII modeling exercise. The European project known as GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data) has produced global-scale re-analyses of air quality for several years, including 2006 (http://gems.ecmwf.int). The GEMS trace gas and aerosol data were made available at 3-hourly intervals on a regular latitude/longitude grid of approximately 1.9° resolution within 2 "cut-outs" from the global model domain. One cut-out was centered over North America and the other over Europe, covering sufficient spatial domain for each modeling group to extract the necessary time- and space-varying (horizontal and vertical) concentrations for their mesoscale model boundaries. Examples of the impact of these boundary concentrations on the AQMEII continental simulations are presented to quantify the sensitivity of the simulations to boundary concentrations. In addition, some participating groups were not able to use the GEMS data and instead relied upon other sources for their boundary concentration specifications. These are noted, and the contrasting impacts of other data sources for boundary data are presented. How one specifies four-dimensional boundary concentrations for mesoscale air quality simulations can have a profound impact on the model results, and hence, this aspect of data preparation must be performed with considerable care.Peer reviewedFinal Accepted Versio

Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR(2): 0.33-0.38). For NO2 CTM improved prediction modestly (adjR(2): 0.58) compared to models without SAT and CTM (adjR(2): 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies

Increase in summer European Ozone amounts due to climate change

Code Ineris : EN-2007-312International audienceThe local and regional distribution of pollutants is significantly influenced by weather patterns and variability along with the spatial patterns of emissions. Therefore, climatic changes which affect local meteorological conditions can alter air quality. We use the regional air quality model CHIMERE driven by meteorological fields from regional climate change simulations to investigate changes in summer ozone mixing ratios over Europe under increased greenhouse gas (GHG) forcing. Using three 30-year simulation periods, we find that daily peak ozone amounts as well as average ozone concentrations substantially increase during summer in future climate conditions. This is mostly due to higher temperatures and reduced cloudiness and precipitation over Europe and it leads to a higher number of ozone events exceeding information and warning thresholds. Our results show a pronounced regional variability, with the largest effects of climate change on ozone concentrations occurring over England, Belgium, Germany and France. The temperature-driven increase in biogenic emissions appears to enhance the ozone production and isoprene was identified as the most important chemical factor in the ozone sensitivity. We also find that summer ozone levels in future climate projections are similar to those found during the exceptionally warm and dry European summer of 2003. Our simulations suggest that in future climate conditions summer ozone might pose a much more serious threat to human health, agriculture and natural ecosystems in Europe, so that the effects of climate trends on pollutant amounts should be considered in future emission control measures

Analyzing the efficiency of short-term air quality plans in European cities, using the CHIMERE air quality model

Regional and local authorities have the obligation to design air quality plans and assess their impacts when concentration levels exceed the limit values. Because these limit values cover both short- (day) and long-term (year) effects, air quality plans also follow these two formats. In this work, we propose a methodology to analyze modeled air quality forecast results, looking at emission reduction for different sectors (residential, transport, agriculture, etc.) with the aim of supporting policy makers in assessing the impact of short-term action plans. Regarding PM10, results highlight the diversity of responses across European cities, in terms of magnitude and type that raises the necessity of designing area-specific air quality plans. Action plans extended from 1 to 3 days (i.e., emissions reductions applied for 24 and 72 h, respectively) point to the added value of trans-city coordinated actions. The largest benefits are seen in central Europe (Vienna, Prague) while major cities (e.g., Paris) already solve a large part of the problem on their own. Eastern Europe would particularly benefit from plans based on emission reduction in the residential sectors; while in northern cities, agriculture seems to be the key sector on which to focus attention. Transport is playing a key role in most cities whereas the impact of industry is limited to a few cities in south-eastern Europe. For NO2, short-term action plans focusing on traffic emission reductions are efficient in all cities. This is due to the local character of this type of pollution. It is important, however, to stress that these results remain dependent on the selected months available for this study.JRC.C.5-Air and Climat

Atmospheric ammonia variability and link with particulate matter formation: a case study over the Paris area

International audienceThe Paris megacity experiences frequent particu- late matter (i.e.PM2.5, particulate matter with a diameter less than 2.5 μm) pollution episodes in spring (March–April). At this time of the year, large numbers of the particles consist of ammonium sulfate and nitrate which are formed from am- monia (NH3) released during fertilizer spreading practices and transported from the surrounding areas to Paris. There is still limited knowledge of the emission sources around Paris, their magnitude, and their seasonality.Using space-borne NH3 observation records of 10 years (2008–2017) and 5 years (2013–2017) provided by the In- frared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) instrument, regional patterns of NH3 variabilities (seasonal and interannual) are derived. Observations reveal identical high seasonal variabil- ity with three major NH3 hotspots found from March to Au- gust. The high interannual variability is discussed with re- spect to atmospheric total precipitation and temperature.A detailed analysis of the seasonal cycle is performed us- ing both IASI and CrIS instrument data, together with out- puts from the CHIMERE atmospheric model. For 2014 and 2015, the CHIMERE model shows coefficients of determina- tion of 0.58 and 0.18 when compared to IASI and CrIS, re- spectively. With respect to spatial variability, the CHIMERE monthly NH3 concentrations in spring show a slight un- derrepresentation over Belgium and the United Kingdom and an overrepresentation in agricultural areas in the FrenchBrittany–Pays de la Loire and Plateau du Jura region, as well as in northern Switzerland. In addition, PM2.5 concentra- tions derived from the CHIMERE model have been evalu- ated against surface measurements from the Airparif network over Paris, with which agreement was found (r2 = 0.56) with however an underestimation during spring pollution events.Using HYSPLIT cluster analysis of back trajectories, we show that NH3 total columns measured in spring over Paris are enhanced when air masses originate from the north-east (e.g. the Netherlands and Belgium), highlighting the impor- tance of long-range transport in the NH3 budget over Paris. Variability in NH3 in the north-east region is likely to impact NH3 concentrations in the Parisian region since the cross- correlation function is above 0.3 (at lag = 0 and 1 d).Finally, we quantify the key meteorological parameters driving the specific conditions important for the formation of PM2.5 from NH3 in the Île-de-France region in spring. Data- driven results based on surface PM2.5 measurements from the Airparif network and IASI NH3 measurements show that a combination of the factors such as a low boundary layer of ∼ 500 m, a relatively low temperature of 5 ◦C, a high relative humidity of 70 %, and wind from the north-east contributes to a positive PM2.5 and NH3 correlation

Long-term health impact assessment of total PM2.5 in Europe during the 1990-2015 period

International audience• PM 2.5 concentrations over Europe were used to perform a health impact assessment during the 1990-2015 period. • Population weighted PM 2.5 concentrations were predicted to have declined by 0.8 µg m −3 y −1 on average. • The number of premature deaths due to exposure to PM 2.5 was predicted to have declined during the 1990-2015 period. • The variability in the predicted number of premature deaths was higher in the 1990s compared to the 2000s

Joint analysis of deposition fluxes and atmospheric concentrations of inorganic nitrogen and sulphur compounds predicted by six chemistry transport models in the frame of the EURODELTAIII project

In the framework of the UNECE Task Force on Measurement and Modelling (TFMM) under the Convention on Long-range Transboundary Air Pollution (LRTAP), the EURODELTAIII project is evaluating how well air quality models are able to reproduce observed pollutant air concentrations and deposition fluxes in Europe. In this paper the sulphur and nitrogen deposition estimates of six state-of-the-art regional models (CAMx, CHIMERE, EMEP MSC-W, LOTOS-EUROS, MINNI and CMAQ) are evaluated and compared for four intensive EMEP measurement periods (25 Feb–26 Mar 2009; 17 Sep–15 Oct 2008; 8 Jan–4 Feb 2007 and 1–30 Jun 2006). For sulphur, this study shows the importance of including sea salt sulphate emissions for obtaining better model results; CMAQ, the only model considering these emissions in its formulation, was the only model able to reproduce the high measured values of wet deposition of sulphur at coastal sites. MINNI and LOTOS-EUROS underestimate sulphate wet deposition for all periods and have low wet deposition efficiency for sulphur. For reduced nitrogen, all the models underestimate both wet deposition and total air concentrations (ammonia plus ammonium) in the summer campaign, highlighting a potential lack of emissions (or incoming fluxes) in this period. In the rest of campaigns there is a general underestimation of wet deposition by all models (MINNI and CMAQ with the highest negative bias), with the exception of EMEP, which underestimates the least and even overestimates deposition in two campaigns. This model has higher scavenging deposition efficiency for the aerosol component, which seems to partly explain the different behaviour of the models. For oxidized nitrogen, CMAQ, CAMx and MINNI predict the lowest wet deposition and the highest total air concentrations (nitric acid plus nitrates). Comparison with observations indicates a general underestimation of wet oxidized nitrogen deposition by these models, as well as an overestimation of total air concentration for all the campaigns, except for the 2006 campaign. This points to a low efficiency in the wet deposition of oxidized nitrogen for these models, especially with regards to the scavenging of nitric acid, which is the main driver of oxidized N deposition for all the models. CHIMERE, LOTOS-EUROS and EMEP agree better with the observations for both wet deposition and air concentration of oxidized nitrogen, although CHIMERE seems to overestimate wet deposition in the summer period. This requires further investigation, as the gas-particle equilibrium seems to be biased towards the gas phase (nitric acid) for this model. In the case of MINNI, the frequent underestimation of wet deposition combined with an overestimation of atmospheric concentrations for the three pollutants indicates a low efficiency of the wet deposition processes. This can be due to several reasons, such as an underestimation of scavenging ratios, large vertical concentration gradients (resulting in small concentrations at cloud height) or a poor parameterization of clouds. Large differences between models were also found for the estimates of dry deposition. However, the lack of suitable measurements makes it impossible to assess model performance for this process. These uncertainties should be addressed in future research, since dry deposition contributes significantly to the total deposition for the three deposited species, with values in the same range as wet deposition for most of the models, and with even higher values for some of them, especially for reduced nitrogen.JRC.C.5-Air and Climat

Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models

This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (-23 %), surface stations (-43 %), or models (-32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (-37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates

MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe

This paper presents the first ensemble modelling experiment in relation to birch pollen in Europe. The seven-model European ensemble of MACC-ENS, tested in trial simulations over the flowering season of 2010, was run through the flowering season of 2013. The simulations have been compared with observations in 11 countries, all members of the European Aeroallergen Network, for both individual models and the ensemble mean and median. It is shown that the models successfully reproduced the timing of the very late season of 2013, generally within a couple of days from the observed start of the season. The end of the season was generally predicted later than observed, by 5 days or more, which is a known feature of the source term used in the study. Absolute pollen concentrations during the season were somewhat underestimated in the southern part of the birch habitat. In the northern part of Europe, a record-low pollen season was strongly overestimated by all models. The median of the multi-model ensemble demonstrated robust performance, successfully eliminating the impact of outliers, which was particularly useful since for most models this was the first experience of pollen forecasting