Prediction of source contributions to surface PM<sub>10</sub> concentrations in European cities: a case study for an episode in December 2016 using EMEP/MSC-W rv4.15 – Part.2 The local urban background contribution

Abstract

Abstract. Despite the progress made in the latest decades, air pollution is still the primary environmental cause of premature death in Europe. The urban population risks more likely to suffer to pollution related to high concentrations of air pollutants such as in particulate matter smaller than 10 µm (PM10). Since the composition of these particulates varies with space and time, the understanding of the origin is essential to determine the most efficient control strategies. A source contribution calculation allows to provide such information and thus to determine the geographical location of the sources (e.g. city or country) responsible for the air pollution episodes. In this study, the calculations provided by the regional EMEP/MSC-W rv4.15 model in a forecast mode, with a 0.25° longitude × 0.125° latitude resolution, and based on a scenario approach, have been explored. To do so, the work has focused on event occurring between 01 and 09 December 2016. This source contribution calculation aims at quantifying over 34 European cities the Local contribution of these PM10, i.e. from the city itself, on an hourly basis. Since the methodology used in the model is based on reduced anthropogenic emissions, compared to a reference run, the choice of the percentage in the reductions has been tested by using three different values (5 %, 15 % and 50 %). The definition of the Local contribution, and thus the definition of the area defining the cities is also an important parameter. The impact of the definition of these urban areas, for the studied cities, was investigated (i.e. 1 model grid cell, 9 grid cells and the grid cells covering the definition given by the Global Administrative Area – GADM). Using a 15 % reduction in the emission and the use of larger cities for our source contribution calculation (e.g. 9 grid cells and GADM), help to reduce the non-linearity in the concentration changes. This non-linearity is observed in the mismatch between the total concentration and the sum of the concentrations from different calculated sources. When this non-linearity is observed, it impacts the NO3−, NH4+ and H2O concentrations. However, the mean non-linearity represents only less than 2 % of the total modelled PM10 calculated by the system. During the studied episode, it was found that 20 % of the predicted PM10 had a Local origin, essentially composed of primary components. 60 % of the hourly PM10 concentrations predicted by the model came from the countries in the regional domain, and they were essentially composed of NO3−. The rest of the PM10 was mainly due to natural sources. It was also shown that the Central European cities were mainly impacted by the surrounding countries while the cities located a little away from the rest of the other European countries (e.g. Oslo and Lisbon) had larger Local contribution. The usefulness of the forecasting tool has also been illustrated with an example in Paris, since the system has been able to predict a local polluted event on 02 December 2016 as documented by local authorities.