Prediction of source contributions to urban background PM10 concentrations in European cities: a case study for an episode in December 2016 – Part.1 The country contributions

Abstract. A large fraction of the urban population in Europe is exposed to particulate matter levels above the WHO guideline. To make more effective mitigation strategies, it is important to understand the influence on particulate matter (PM) from pollutants emitted in different European nations. In this study, we evaluate a source apportionment forecasting system aimed to assess the domestic and transboundary contributions to PM in major European cities for an episode in December 2016. The system is composed of two models (EMEP/MSC-W rv4.15 and LOTOS-EUROS v2.0) which allows to consider differences in the source attribution. We also compared the PM10 concentrations and both models present satisfactory agreement in the 4day-forecasts of the surface concentrations, since the hourly concentrations can be highly correlated with in-situ observations. The correlation coefficients reach values up to 0.58 for LOTOS-EUROS and 0.50 for EMEP for the urban stations; and 0.58 for LOTOS-EUROS and 0.72 for EMEP for the rural stations. However, the models under-predict the highest hourly concentrations measured by the urban stations (mean underestimation by 36 %), predictable with the relatively coarse model resolution used (0.25° longitude × 0.125° latitude). For the source receptor calculations, the EMEP/MSC-W model uses a scenario having reduced anthropogenic emissions and then it is compared to a reference run where no changes are applied. Different percentages (5 %, 15 % and 50 %) in the reduced emissions were used to test the robustness of the methodology. The impact of the different ways to define the urban area for the studied cities was also investigated (i.e. 1 model grid cell, 9 grid cells and the grid cells covering the definition given by the Global Administrative Area – GADM). We found that by combining the use of the 15 % factor and of a larger domain for the city edges (9 grid cells or GADM), it helps to reduce the impact of non-linearity on the chemistry which is seen in the mismatch between the total concentration and the sum of the concentrations from different calculated sources. Even limited, this non-linearity is observed in the NO3−, NH4+ and H2O concentrations, which is related to gas-aerosol partitioning of the species. The use of a 15 % factor and of a larger city domain also gives a better agreement in the determination of the main country contributors between both country source receptor calculations. During the studied episode, dominated by the influence of the domestic emissions for the 34 European cities investigated and occurring from December 01st to 09th 2016, the two models agree 68 % of the time (on hourly resolution) on the country, having been the dominant contributor to PM10 concentrations. 75 % of the hourly predicted PM10 concentrations by both models, have the same top 5 main country contributors. Better results are found in the determination the dominant country contributor for the primary component (70 % for POM and 80 % for EC) than for the secondary inorganic aerosols (50 %).