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. Despite the progress made in the latest decades, air pollution is still theprimary environmental cause of premature death in Europe. The urbanpopulation risks more likely to suffer to pollution related to highconcentrations of air pollutants, such as in particulate matter smaller than10 µm (PM10). Since the composition of these particulates varieswith space and time, the understanding of the origin is essential todetermine the most efficient control strategies. A source contribution calculation allows us to provide such information andthus to determine the geographical location of the sources (e.g. city orcountry) responsible for the air pollution episodes. In this study, thecalculations provided by the regional European Monitoring and EvaluationProgramme/Meteorological Synthesizing Centre – West (EMEP/MSC-W) rv4.15 model in a forecastmode, with a 0.25∘ longitude × 0.125∘ latituderesolution, and based on a scenario approach, have been explored. To do so,the work has focused on event occurring between 1 and 9 December 2016.This source contribution calculation aims at quantifying over 34 Europeancities, the “city” contribution of these PM10, i.e. from the cityitself, on an hourly basis. Since the methodology used in the model is basedon reduced anthropogenic emissions, compared to a reference run, the choiceof the percentage in the reductions has been tested by using three differentvalues (5 %, 15 %, and 50 %). The definition of the “city”contribution, and thus the definition of the area defining the cities isalso an important parameter. The impact of the definition of these urbanareas, for the studied cities, was investigated (i.e. one model grid cell, ninegrid cells and the grid cells covering the definition given by the globaladministrative area – GADM). Using a 15 % reduction in the emission and larger cities forour source contribution calculation (e.g. nine grid cells and GADM) helps toreduce the non-linearity in the concentration changes. This non-linearity isobserved in the mismatch between the total concentration and the sum of theconcentrations from different calculated sources. When this non-linearity isobserved, it impacts the NO3-, NH4+, and H2Oconcentrations. However, the mean non-linearity represents only less than2 % of the total modelled PM10 calculated by the system. During the studied episode, it was found that 20 % of the surfacepredicted PM10 had been from the “city”, essentially composed ofprimary components. In total, 60 % of the hourly PM10 concentrations predictedby the model came from the countries in the regional domain, and they wereessentially composed of NO3- (by ∼ 35  %). The twoother secondary inorganic aerosols are also important components of this“rest of Europe” contribution, since SO42- and NH4+represent together almost 30 % of this contribution. The rest of thePM10 was mainly due to natural sources. It was also shown that thecentral European cities were mainly impacted by the surrounding countrieswhile the cities located a bit away from the rest of the other Europeancountries (e.g. Oslo and Lisbon) had larger “city” contributions. Theusefulness of the forecasting tool has also been illustrated with an examplein Paris, since the system has been able to predict the primary sources of alocal polluted event on 1–2 December 2016, as documented by localauthorities.

geoscientific model development

Prediction of source contributions to urban background PM&amp;lt;sub&amp;gt;10&amp;lt;/sub&amp;gt; concentrations in European cities: a case study for an episode in December 2016 using EMEP/MSC-W rv4.15 – Part 2: The city contribution