Impact of Physics Parameterizations on High-Resolution Air Quality Simulations over the Paris Region

The accurate simulation of meteorological conditions, especially within the planetary boundary layer (PBL), is of major importance for air quality modeling. In the present work, we have used the Weather Research and Forecast (WRF) model coupled with the chemistry transport model (CTM) CHIMERE to understand the impact of physics parameterizations on air quality simulation during a short-term pollution episode on the Paris region. A lower first model layer with a 4 m surface layer could better reproduce the transport and diffusion of pollutants in a real urban environment. Three canopy models could better reproduce a 2 m temperature (T2) in the daytime but present a positive bias from 1 to 5 °C during the nighttime; the multi-urban canopy scheme “building effect parameterization” (BEP) underestimates the 10 m windspeed (W10) around 1.2 m s−1 for the whole episode, indicating the city cluster plays an important role in the diffusion rate in urban areas. For the simulation of pollutant concentrations, large differences were found between three canopy schemes, but with an overall overestimation during the pollution episode, especially for NO2 simulation, the average mean biases of NO2 prediction during the pollution episode were 40.9, 62.2, and 29.7 µg m−3 for the Bulk, urban canopy model (UCM), and BEP schemes, respectively. Meanwhile, the vertical profile of the diffusion coefficients and pollutants indicated an important impact of the canopy model on the vertical diffusion. The PBL scheme sensitivity tests displayed an underestimation of the height of the PBL when compared with observations issued from the Lidar. The YonSei University scheme YSU and Boulac PBL schemes improved the PBL prediction compared with the Mellor–Yamada–Janjic (MYJ) scheme. All the sensitivity tests, except the Boulac–BEP, could not fairly reproduce the PBL height during the pollution episode. The Boulac–BEP scheme had significantly better performances than the other schemes for the simulation of both the PBL height and pollutants, especially for the NO2 and PM2.5 (particulate matter 2.5 micrometers or less in diameter) simulations. The mean bias of the NO2, PM2.5, and PM10 (particulate matter 10 micrometers or less in diameter) prediction were −5.1, 1.2, and −8.6 µg m−3, respectively, indicating that both the canopy schemes and PBL schemes have a critical effect on air quality prediction in the urban region.</jats:p

High resolution air quality simulation over Europe with the chemistry transport model CHIMERE

A high resolution air quality simulation (0.125° × 0.0625° horizontal resolution) performed over Europe for the year 2009 has been evaluated using both rural and urban background stations available over most of the domain. Using seasonal and yearly mean statistical indicators such as the correlation index, the fractional bias and the root mean squared error; we interpret objectively the performance of the simulation. Positive outcomes are: a very good reproduction of the daily variability at UB sites for O3 (R =0.73) as well as for NO2 (R =0.61); a very low bias calculated at UB stations for PM2.5 (FB = −6.4%) and PM10 concentrations (FB = −20.1%). Conversely, main weaknesses in model performance include: the underestimation of the NO2 daily maxima at UB site (FB = −53.6%); an overall underestimation of PM10 and PM2.5 concentrations observed over Eastern European countries (e.g. Poland); the overestimation of sulphates concentrations at spring time (FB = 53.7%); finally, over the year, total nitrate and ammonia concentrations are better reproduced than nitrate and ammonium aerosol phase compounds. Obtained results suggest that, in order to improve the model performances, efforts should focus on the improvement of the emission inventory quality for Eastern Europeans countries and the improvement of a specific parameterisation in the model to better account for the urban effect on meteorology and air pollutants concentrations.JRC.H.2-Air and Climat

Characterizing environmental geographic inequalities using an integrated exposure assessment

International audienceBackground: At a regional or continental scale, the characterization of environmental health inequities (EHI) expresses the idea that populations are not equal in the face of pollution. It implies an analysis be conducted in order to identify and manage the areas at risk of overexposure where an increasing risk to human health is suspected. The development of methods is a prerequisite for implementing public health activities aimed at protecting populations.Methods: This paper presents the methodological framework developed by INERIS (French National Institute for Industrial Environment and Risks) to identify a common framework for a structured and operationalized assessment of human exposure. An integrated exposure assessment approach has been developed to integrate the multiplicity of exposure pathways from various sources, through a series of models enabling the final exposure of a population to be defined.Results: Measured data from environmental networks reflecting the actual contamination of the environment are used to gauge the population's exposure. Sophisticated methods of spatial analysis are applied to include additional information and take benefit of spatial and inter-variable correlation to improve data representativeness and characterize the associated uncertainty. Integrated approaches bring together all the information available for assessing the source-to-human-dose continuum using a Geographic Information System, multimedia exposure and toxicokinetic model.Discussion: One of the objectives of the integrated approach was to demonstrate the feasibility of building complex realistic exposure scenarios satisfying the needs of stakeholders and the accuracy of the modelling predictions at a fine spatial-temporal resolution. A case study is presented to provide a specific application of the proposed framework and how the results could be used to identify an overexposed population

Characterizing Environmental Inequalities Using Integrated Exposure Assessment and Spatial Approach

Abstract BackgroundAt a regional or continental scale, the characterization of environmental health inequalities (EHI) expresses the idea that populations are not equal in the face of pollution. It implies the analysis in order to identifying and managing areas at risk of overexposure where increasing risk to human health is suspected. The development of methods is a prerequisite for the implementation of public health actions aimed at the protection of populations.MethodsThis paper presents the methodological framework developed by INERIS (French national institute for industrial environment and risks) to identify a common framework for conceptualizing and operationalizing environmental exposures as an important step towards articulating a science of EHI. We develop an integrated exposure assessment approach capable to integrate the multiplicity of exposure pathways from various sources, through a series of models up to the internal exposure.ResultsMeasured data from environmental networks reflecting the actual contamination of the environment are reused to characterize the population's exposure. Sophisticated methods of spatial analysis are applied to include additional information and take benefit from spatial and inter-variable correlation to improve data representativeness and characterize associated uncertainty. Integrated approaches bring together all information necessary for assessing the source-to-human-dose continuum using Geographic Information System, multimedia exposure and toxicokinetic model. ConclusionThis framework could be used for many purposes, such as mapping EHI, identifying vulnerable populations and determinants of exposure to manage and plan remedial actions and assessing spatial relationships between health and environmental to identify factors that influence the variability of disease patterns.</jats:p

Characterizing environmental geographic inequalities using an integrated exposure assessment

Abstract Background At a regional or continental scale, the characterization of environmental health inequities (EHI) expresses the idea that populations are not equal in the face of pollution. It implies an analysis be conducted in order to identify and manage the areas at risk of overexposure where an increasing risk to human health is suspected. The development of methods is a prerequisite for implementing public health activities aimed at protecting populations. Methods This paper presents the methodological framework developed by INERIS (French National Institute for Industrial Environment and Risks) to identify a common framework for a structured and operationalized assessment of human exposure. An integrated exposure assessment approach has been developed to integrate the multiplicity of exposure pathways from various sources, through a series of models enabling the final exposure of a population to be defined. Results Measured data from environmental networks reflecting the actual contamination of the environment are used to gauge the population’s exposure. Sophisticated methods of spatial analysis are applied to include additional information and take benefit of spatial and inter-variable correlation to improve data representativeness and characterize the associated uncertainty. Integrated approaches bring together all the information available for assessing the source-to-human-dose continuum using a Geographic Information System, multimedia exposure and toxicokinetic model. Discussion One of the objectives of the integrated approach was to demonstrate the feasibility of building complex realistic exposure scenarios satisfying the needs of stakeholders and the accuracy of the modelling predictions at a fine spatial-temporal resolution. A case study is presented to provide a specific application of the proposed framework and how the results could be used to identify an overexposed population. Conclusion This framework could be used for many purposes, such as mapping EHI, identifying vulnerable populations and providing determinants of exposure to manage and plan remedial actions and to assess the spatial relationships between health and the environment to identify factors that influence the variability of disease patterns. </jats:sec

Strengths and weaknesses of the FAIRMODE benchmarking methodology for the evaluation of air quality models

The Forum of Air Quality Modelling in Europe (FAIRMODE) was launched in 2007 to bring together air quality modellers and users in order to promote and support the harmonised use of models by EU Member States, with emphasis on model application under the European Air Quality Directive. In this context, a methodology for evaluating air quality model applications has been developed. This paper presents an analysis of the strengths and weaknesses of the FAIRMODE benchmarking approach, based on users’ feedback. European wide, regional and urban scale model applications, developed by different research groups over Europe, have been taken into account. The analysis is focused on the main pollutants under the Air Quality Directive, namely PM10, NO2 and O3. The different case studies are described and analysed with respect to the methodologies applied for model evaluation and quality assurance. This model evaluation intercomparison demonstrates the potential of a harmonised evaluation and benchmarking methodology. A SWOT analysis of the FAIRMODE benchmarking approach is performed based on feedback from users of the tool. This analysis helps to identify the main advantages and value of this model evaluation benchmarking approach compared with other methodologies, in addition to highlighting requirements for future development.JRC.C.5-Air and Climat