Table S1. Description of the 19 sites with data on aerosol chemical composition (Fig. 1 in the article).

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1 Table S1. Description of the 19 sites with data on aerosol chemical composition (Fig. 1 in the article). Site number Name of site Acrony m Lon Lat alt (m a.s.l) period samples 1 High Altitude Site ASC winter&su mmer 2 Oasi Le Bine OB winter&su mmer 3 Milano (Torre Sarca) MI July/winter &summer 24 hrs 24 hrs 12hrs, day&night/ 24 hrs 4 Ispra IT whole year 24 hrs 5 Montelibretti MTL or IT July/whole year 12hrs, day&night/ 24 hrs 6 Rende REN July 12hrs, day and night 7 San Pietro Capofiume SPC July 12hrs, day and night 8 Monte Cimone CMN July 12hrs, day and night 9 Pavia Sannazzaro winter 24 hrs 10 Brescia winter 24 hrs 11 Cantù winter 24 hrs 12 Lodi Abbadia winter 24 hrs 13 Mantova Winter 24 hrs 14 Milan-ks Winter 24 hrs 15 Milan-ub Winter 24 hrs 16 Milan-S Winter 24 hrs 17 Saronno Winter 24 hrs 18 Sondrio Winter 24 hrs 19 Bergamo Winter 24 hrs 1. Effect of grid resolution on meteorological parameters. Figures S1 and S2 show the annual correlation and root mean square error (rmse) for hourly temperature (T), relative humidity (RH) and wind speed (WS) simulated with RAMS at 20 and 4 km grid resolutions, respectively. Values in the Figures represent the average for all the stations.

2 The observations used in this operational evaluation were not used in the nudging procedure, and were retrieved from some Environmental Regional Agencies local networks. The comparison shows almost no differences in these model skills due to the increase of grid resolution. This may be explained by the fact that the observations were mostly available at stations located in Po Valley which is characterised by rather homogeneous meteorological conditions, with smooth gradients in the meteorological fields. corr T RH WS 20km 4km rmse km 4km 5 0 T RH WS Figure S1. Annual correlation and root mean square error (rmse) for temperature (T), relative humidity (RH) and wind speed (WS) simulated with RAMS for 20 and 4 km grid resolutions. Moreover, the values of model skills are in good agreement with the benchmarks proposed in Tesche et al. (2002) for statistical indicators of meteorological model simulations to be used in air quality modelling. Figure S2 shows the annual averages for each site of simulated (20 and 4 km grid resolutions) and measured temperature (T), relative humidity (RH), wind speed (WS) and accumulated precipitation (PREC). It can be observed that the change of grid resolution influence all meteorological

3 parameters at many stations, in particular T and RH. The use of 4km grid resolution leads to an underestimation of temperatures and an overestimation of relative humidity at most of the stations with low values for these parameters (i.e. high altitude sites, usually located over complex orography). The tendency of meteorological model to overestimate wind speed remains almost unchanged for both resolutions. Accumulated precipitation over the Po Valley (observed values generally up to mm) tends to be underestimated both at 20 and 4 km resolution, but there are several stations overestimated. Over Alps, accumulated precipitation (observed values above mm) is slightly underestimated at both resolutions. More specific investigation would be needed, using more specific statistical scores but here we limit the discussion to general aspects. Figure S2. Annual averages of simulated (20 and 4 km grid resolutions) and measured temperature (T), relative humidity (RH), wind speed (WS) and accumulated precipitation.

The lack of collocated meteorological measurements with measurements of aerosol chemistry and gas precursors does not allow a more in depth analysis of the impact of meteorology on aerosol chemical

4 The lack of collocated meteorological measurements with measurements of aerosol chemistry and gas precursors does not allow a more in depth analysis of the impact of meteorology on aerosol chemical composition. This is a known limitation of air quality studies such as Fountoukis et al. (2013), Stroud et al. (2011), etc. 2. Effect of grid resolution on spatial distribution of anthropogenic emissions. Elemental carbon Primary organic aerosol Figure S3. Annual averages of differences between flux emissions (µg m 2 s 1 ) at 4 km and 20 grid resolutions for elemental carbon and primary organic aerosol. Figure S3 shows the differences in emissions flux for elemental carbon (EC) and primary organic aerosol due to the change of grid resolution. The differences are positive for 4 km grid resolution along the highways and in the cities and negative in the other places. This behaviour is explained by the fact that the same amount of pollutant emitted in atmosphere by traffic and residential combustion (SNAP 2 and 7) follows the map of roads and the distribution of population that are more detailed for 4 km grid resolution. To some extent, this is true also for anthropogenic part of PM2.5 and PM10 and for some part of secondary inorganic and organic aerosol since their precursor emissions SO 2, NO 2, toluene, xylene are distributed with the same proxy as aerosol species.

5 Table S2. Mass concentrations of aerosol species (g m 3 ) shown in Fig. 2 in the article and the differences between the simulations (M4-M20) and between the simulations and observations (M20-O; M4-O). O means MI_Perrone, OB_Perrone, ASC_Perrone. winter 20_MI 4_MI MI_Perrone 20_OB 4_OB OB_Perrone. 20_ASC 4_ASC ASC_Perrone SO NO NH OC EC unknown summer 20_MI 4_MI MI_Perrone 20_OB 4_OB OB_Perrone 20_ASC 4_ASC ASC_Perrone SO NO NH OC EC unknown winter M20-O M4-O M4-M20 M20-O M4-O M4-M20 M20-O M4-O M4-M20 SO NO NH OC EC unknown summer M20-O M4-O M4-M20 M20-O M4-O M4-M20 M20-O M4-O M4-M20 SO NO NH OC EC unknown

$Chemical composition of fine fraction aerosol during summer and winter, for 4 km and 20 km simulations$

6 Figure S4. Chemical composition of fine fraction aerosol during summer and winter, for 4 km and 20 km simulations at high altitude typical remote site in the Alps (ASC): comparison with Perrone et al. (2012).

7 Table S3. Mass concentrations of aerosol species (g m 3 ) shown in Fig. 3 in the article and the differences between the simulations (M4-M20) and between the simulations and observations (M20-O; M4-O). O means MI_Carbone, SPC_Carbone. CMN_Carbone, MTL_Carbone, REN_Carbone. day 20_MI 4_MI MI_Car 20_SPC 4_SPC SPC_Ca 20_CM 4_CMN CMN_C 20MTL 4_MTL MTL_C 20_REN 4_REN REN_Ca bone rbone N arbone arbone rbone SS SO NO NH OC EC night 20_MI 4_MI MI_Car bone 20_SPC 4_SPC SPC_Ca rbone 20_CM N 4_CMN CMN_C arbone 20MTL 4_MTL MTL_C arbone 20_REN 4_REN REN_Ca rbone SS SO NO NH OC EC day M20-O M4-O M4-M20 M20-O M4-O M4-M20 M20-O M4-O M4-M20 M20-O M4-O M4-M20 M20-O M4-O M4-M20 SS SO NO NH OC EC night M20-O M4-O M4-M20 M20-O M4-O M4-M20 M20-O M4-O M4-M20 M20-O M4-O M4-M20 M20-O M4-O M4-M20 SS SO NO NH OC+ EC

8 3. Statistical indexes. Correlation (corr) 1 (1) where M denotes modelled value, O denotes observed value, N is the number of paired values considered, O and M are the standard deviation of measurements and model, respectively. Factor of Two (fac2) (2) Bias (bias) 1 (3) Mean Absolute Gross Error (mage) 1 (3) Mean Absolute Normalised Gross Error (mange) 1 (5) Root Mean Square Error (rmse) 1 (64)

9 References Tesche, T. W. and D. E. McNally, Operational evaluation of the MM5 meteorological model over the continental United States. Task Order 4TCG Prepared for Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency.

History and perspective of air quality in Milan and the Po Valley: scientific and management challenges

History and perspective of air quality in Milan and the Po Valley: scientific and management challenges Guido Lanzani g.lanzani@arpalombardia.it Head of Air Quality Unit Environmental Monitoring Area ARPA