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We present a simple framework to easily pre-select the most essential data for accurately forecasting the concentration of the pollutant PM 10 , based on pollutants observations for the years 2002 until 2006 in the metropolitan region of Lisbon, Portugal. Starting from a broad panoply of different data sets collected at several meteorological stations, we apply a forward stepwise regression procedure that enables us not only to identify the most important variables for forecasting the pollutant but also to rank them in order of importance. We argue the importance of this variable ranking, showing that the ranking is very sensitive to the urban spot where measurements are taken. Having this pre-selection, we then present the potential of linear and non-linear neural network models when applied to the concentration of pollutant PM 10 . Similarly to previous studies for other pollutants, our validation results show that non-linear models in average perform as well or worse as linear models for PM 10 . Finally, we also address the influence of Circulation Weather Types, characterizing synoptic scale circulation patterns and the concentration of pollutants.

A large outbreak of Legionnaires' disease occurred in November 2014 nearby Lisbon, Portugal. This epidemic infected 377 individuals by the Legionella pneumophila bacteria, resulting in 14 deaths. The primary source of transmission was contaminated aerosolized water which, when inhaled, lead to atypical pneumonia. The unseasonably warm temperatures during October 2014 may have played a role in the proliferation of Legionella species in cooling tower systems. The episode was further exacerbated by high relative humidity and a thermal inversion which limited the bacterial dispersion. Here, we analyze if the Legionella outbreak event occurred during a situation of extreme potential recirculation and/or stagnation characteristics. In order to achieve this goal, the Allwine and Whiteman approach was applied for a hindcast simulation covering the affected area during a near 20-year long period (1989-2007) and then for an independent period covering the 2014 event (15 October to 13 November 2014). The results regarding the average daily critical transport indices for the 1989-2007 period clearly indicate that the airshed is prone to stagnation as these events have a dominant presence through most of the study period (42%), relatively to the occurrence of recirculation (18%) and ventilation (17%) events. However, the year of 2014 represents an exceptional year when compared to the 1989-2007 period, with 53 and 33% of the days being classified as under stagnation and recirculation conditions, respectively.

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