Evaluation of EU air quality standards through modeling and the FAIRMODE benchmarking methodology

Abstract

We evaluate air quality modeling over the East Mediterranean using the benchmarking methodology developed in the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE). FAIRMODE aims to provide a harmonized approach of model evaluation for regulatory purposes. We test the methodology by assessing the performance of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) against ground-based air quality observations over Cyprus, a member state of the European Union. Two nested domains are used (at 50- and 10-km horizontal grid spacing) with the comparison performed over the innermost domain. We consider performance indicators reflecting regulations for air quality standards (maximum daily 8-hourly mean ozone, hourly nitrogen dioxide, and daily fine particulate matter concentrations). The WRF-Chem model is found to satisfy the proposed performance objectives regarding ozone and NO2, though it underestimates the latter in urban areas possibly due to uncertainties in emission inventories. Fine particulate matter is well represented by the model, except on days with strong influence from natural sources, highlighting the necessity for fine-tuning dust mobilization and transport in the region. The objectives are fulfilled even though discrepancies exist between model and observations. Our results indicate the need for more stringent performance criteria at relatively low concentrations. Overall, we find that the methodology provides in-depth information and relevant statistical metrics to guide air quality and model assessments for monitoring compliance with the EU Air Quality Directives and other guidelines to limit the impact of air pollution on human health and ecosystems.Electronic supplementary materialThe online version of this article (10.1007/s11869-018-0631-z) contains supplementary material, which is available to authorized users.

Differences in the Estimation of Wildfire-Associated Air Pollution by Satellite Mapping of Smoke Plumes and Ground-Level Monitoring

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Characterizing the regional concentrations and seasonality of the emerging pollutant decamethylcyclopentasiloxane (D5) using a WRF+CHIMERE modeling approach

“…In evaluating the modeled atmospheric concentrations, a number of statistical parameters were considered. Spatial correlation coefficient (r), standard deviation (SD), and mean bias (MB) values are commonly used by the modeling community (e.g., Baldasano et al, 2011;Im et al, 2018;Monteiro et al, 2018;Kushta et al, 2019, among many others) and were therefore selected according to the criteria of . Spatial-r is used to assess the ability of the model to capture the spatial pattern of D5 observations.…”

Updated national emission inventory and comparison with the Emissions Database for Global Atmospheric Research (EDGAR): case of Lebanon

“…In addition, EDGAR was considered as a validation basis for studies related to emissions of different pollutants using various modeling approaches (Dentener et al 2002;Bergamaschi et al 2010). However, a comparison between EDGAR and national developed emission inventories showed that even though EDGAR can be applicable for continental-scale modeling, it is not recommended for regional to local air quality simulations, and a more complete and up-to-date national emission inventory should be used instead (Madrazo et al 2018;Puliafito et al 2017;van Amstel et al 1999;Hristov et al 2017;Liousse et al 2019;Georgiou et al 2020;Kushta et al 2019).…”