PAHs modelling over urban area of Rome: integration of models results with experimental data

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1 PAHs modelling over urban area of Rome: integration of models results with experimental data Claudio Gariazzo 1, Camillo Silibello 2, Sandro Finardi 2, Paola Radice 2, Alessio D Allura 2, Monica Gherardi 1, Angelo Cecinato 3 1 INAIL Research Center, Via Fontana Candida 1, 4 Monteporzio Catone (RM), Italy 2 Arianet, via Gilino 9, 2128 Milano, Italy 3 CNR-IIA, Via Salaria km 29.3, I-15 Montelibretti (RM), Italy With the contribution of the LIFE financial scheme of the European Community ITM 213 NATO/SPS International Technical Meeting on Air Pollution Modeling and its Application 26-3 August 213, Miami, Florida USA

2 OUTLINE The EXPAH EU LIFE+ project PAHs monitoring Development of a regional PAHs emission inventory PAHs modelling and study design Modelling results and comparison with observations Limits and solutions undertaken Conclusions

3 The LIFE+ EXPAH Project Main objective Quantification of the population exposure to PAHs in urbanized areas and determination of their impact on human health. The project is focused on elderly people and school-age children in the city of Rome. The project has a duration of 3.5 years (from 1/1/21 to 3/6/214) and is based on an integrated approach where: Measurements; modeling techniques; epidemiological investigations permit to: Assess the actual exposure in different living environments obtain maps of ambient concentration and population exposure; estimate potential health effects on the target population; perform mitigation scenarios analyses.

4 PAH monitoring

5 UTM32 North [km] PAH monitoring Seasonal field campaigns Winter/summer weekly campaigns PAHs indoor/outdoor monitoring: 9 homes; 6 schools; 2 offices; 2 cars; 1 bus PAH outdoor monitoring (AQ network): 7 stations (A Lazio) Personal PAH sampling on volunteers (elderly and children) outdoor schools homes indoor Legend: Hxx = homes; Monitoring Ixx = schools; sites Oxx = Offices 4654 cars Rome bus personal exposure UTM32 East [km]

6 Development of a regional PAHs emission inventory

7 Development of a regional PAHs emission inventory Data sources National emission inventory ISPRA 25 - Top-down approach Province (NUTS3) resolution total PAHs only included ISPRA historical series (yearly national total emissions) EMEP & TNO PAHs European inventories A Lombardia investigation on wood burning for house heating in Italy natural gas supply in Lazio Region municipalities passengers and goods movement to/from Rome airports ships movement to/from Civitavecchia port traffic flows for Lazio Region and Rome road network Lazio point sources inventory (reference year 29) Data processing on Lazio Region emission inventory Emissions downscaling from provincial to municipal resolution Update to the reference year 29 Speciation of total PAHs in 4 congeners (UNECE POPs Protocol) based on EMEP/EEA 29 and COPERT IV methodology Estimation of wood burning emission Space distribution of residential heating emissions based on municipalities reached by natural gas distribution and fuel sold Bottom-up transport emission estimation Comparison of EMEP, TNO and ISPRA PAHs emission inventories

8 Titolo TNO 25 ISPRA25 EMEP25 TNO 25 ISPRA25 EMEP25 Development of a regional PAHs emission inventory TNO 25 ISPRA25 EMEP25 TNO 25 ISPRA25 EMEP25 KG/ANNO Comparison of 25 PAHs emission inventories over Italy: ISPRA (National); EMEP & TNO (European). 7 6 by activity sector 16 by congeners TOTALE WASTE _NRT 5_ROT _SPU 3_IND 2_RCO 1_PHP 8 6 Indeno BkF BbF BaP 4 2 BaP BbF BkF Indeno 1- PHP Public heat and power 2- RCO Residential combustion and other; 3- IND Industry; 4- SPU Solvent and product use; 5- ROT Road transport 6- NRT Non-Road transport 7- WAS Waste treatment TNO 25 ISPRA25 EMEP25 Significant differences among different emission inventories for: Total PAHs emission PAHs emission for activity sector and speciation profiles

9 Development of a regional PAHs emission inventory kg/year Detailed calculation of traffic PAHs emission over: Lazio Region and city of Rome Lazio region Rome Contribution of the emission sectors to PAHs emission over Lazio Region BaP Nature Agriculture Forestry & Land Use Change Waste Treatment Other Transport & Mobile Machinery Road Transport Comb in Residential Energy Production 4 2 B[a]P B[b]F B[k]F indeno Total PAHs yearly emission over Rome metropolitan area Dominant contribution to PAHs emissions from biomass combustion (domestic heating and stubbles combustion); High uncertainties about the amount burned materials and its spatial distribution; Domestic heating spatialized over built area using municipalities reached by natural gas distribution and fuel sold; Better information on amount and locations needed

10 PAHs modeling and study design

11 Flexible Air quality Regional Model (FARM) (included in the EEA Model Documentation System) Main features and developments: Emission of pollutants from area and point sources, with plume rise calculation and mass assignment to vertical grid cells 3D dispersion by advection and turbulent diffusion PAHs modeling and study design Transformation of chemical species by gas-phase chemistry, with flexible mechanism configuration (SAPRC-99, POPs through KPP) Treatment of PM 1 and PM 2.5 (aero inorganic equilibrium module, aero3 modal aerosol module) Dry removal of pollutants dependent on local meteorology and land-use Removal through precipitation scavenging processes One- and two-way nesting on arbitrary number of grids Parallel processing using OpenMP paradigm Inclusion of data assimilation techniques Online calculation of photolysis rates using TUV model (Tropospheric Ultraviolet and Visible radiation model; Madronich et al, 1989) The updated version of FARM includes: PAHs considered by the CLRTAP (Convention on Long-range Transboundary Air Pollution) Protocol on POPs (http://www.unece.org/env/lrtap/pops_h1.ht ml): B[a]P, B[b]F, B[k]F and I_P; Pollutants treated by the Directive 28/5/EC of the European Parliament and of the Council of 21 May 28 on ambient air quality and cleaner air for Europe (http://eurlex.europa.eu/lexuriserv/lexurise rv.do?uri=oj:l:28:152:1:44:en:pdf ): Hg, Lead, Arsenic, Cadmium and Nickel.

12 PAHs modeling and study design SAPRC99 gas-phase chemical mechanism for PAHs The following gas-phase species have been included : benzo(a)pyrene (B[a]P); benzo(b)fluoranthene (B[b]F); benzo(k)fluoranthene (B[k]F); indeno(1,2,3-cd)pyrene (I_P); Gas-phase reactions, involving PAHs, added to SAPRC99. Degradation process of PAHs in the atmosphere is considered as the gas-phase reaction of these pollutants with hydroxyl radical. All other reactions are neglected. Reaction Reaction rate [cm 3 molec -1 s -1 ] B[a]P + OH products B[b]F + OH products B[k]F + OH products I_P + OH products Reference Meylan and Howard, 1993 Hg + O 3.5HgO +.5HgAER ARR( ,1256.5) (298 K) Xie et al., 28; Jung et al., 29; Hg + OH.5HgO +.5HgAER HgAER (condensable mercury) from

13 PAHs modeling and study design AERO3 (from CMAQ) Modal approach, lognormal size distribution: Aitken (I) mode (-.1 µm) Accumulation (J) mode ( µm) Coarse (PM1 - PM2.5) Aerosol processes: Nucleation (Kulmala et al., 1998 ); Coagulation (Whitby et al., 1991 and Binkowski and Shankar, 1995 ); Gas/particle mass transfer ISORROPIA equilibrium model (Nenes et al., 1998) ; SOA treatment (Odum et al., 1996; Shell et al. 21) Following aerosol-phase PAHs species have been included in the AERO3 aerosol module: Aitken (I) and accumulation (J) modes: Benzo(a)pyrene (AB[a]PI, AB[a]PJ); Benzo(b)fluoranthene (AB[b]FI, AB[b]FJ); Benzo(k)fluoranthene (AB[k]FI, AB[k]FI); Indeno(1,2,3-cd)pyrene (AI_PI, AI_PJ); Aerosol PAHs processes involved: aerosol-gas partitioning; deposition and scaveging Degradation of aerosol of B[a]P by Ozone not included.

14 PAHs modeling and study design Aerosol-gas partitioning of PAHs The fraction of POPs sorbed on tropospheric aerosol particles is computed following the approach proposed in Aulinger et al., 27. The particulate PAHs were introduced in both Aitken and accumulation modes, as in the aerosol module AERO3 the two modes actually contains organic matter and water. The concentration of the compound that is bound to aerosols in each mode is calculated from: a i i where a i is the particulate concentration of the compound in mode i (i=i,j e.g. Aitken and accumulation), g is the gaseous concentration and i is the particulate fraction of the compound in mode i due to the sum of three different sorption processes: adsorption (ad); absorption into organic carbon (ab,oc); ad ab, OC ab, aq absorption into aerosol water (ab,aq) i i i i a i g ad i ab,oc I,J ab,aq i c i pol c i OC K TSP P,i i OC 1 K TSP aq K P,i 1 K P,i TSP aq P,i i TSP i i ADSORPTION (Junge-Pankow model) ABSORPTION INTO OC (Octanol-air partitioning) ABSORPTION INTO AEROSOL WATER

15 PAHs modelling and study design Period: June 211 May 212; Domains (Lazio/Rome): FARM simulations Lazio Region: 4 km resolution, 61 x 51 cells; Rome: 1 km resolution, 6 x 6 cells; 16 vertical levels, between 2 and 1 m; Meteorology: provided by RAMS driven by ECMWF analyses; Anthropogenic Emissions: National (ISPRA, surrounding sources) and Lazio Region inventories; Natural Emissions: Soil erosion and resuspension, sea salts, biogenic; Boundary conditions: FARM over Italy (12 km resolution). Lazio region Rome target

16 PAHs modelling and study design RAMS FARM Grid 2 Grid 1 FARM Grid 1 Grid 2 xy (km) Nx Ny Nz A nested simulation with horizontal grid space of 4 km over Lazio Region and 1 km over Rome conurbation FARM Grid 3 Grid A background model simulation integrating European and Italian PAHs emissions (horizontal resolutions from 48 to 2 km) for the investigated period (from June 211 to May 212) to provide boundary conditions (BC) over Lazio Region domain Grid 3 (Lazio) xy (km) 4 1 Nx 61 6 Ny 51 6 Nz Grid 4 (Rome, target)

17 Modelling results and comparison with observations

18 [ng m -3 ] [ng m -3 ] FRA CGU BEL MAG FRA CGU BEL MAG FRA CGU BEL MAG FRA CGU BEL MAG [ng m -3 ] [ng m -3 ] [ng m -3 ] FRA CGU BEL MAG [ng m -3 ] FRA CGU BEL MAG FRA CGU BEL MAG.1. Modelling results and comparison with observations BaP BbF BkF IP Measured (14/5/212) Computed (14/5/212) BaP BbF BkF IP Measured (12/12/211) Computed BaP (12/12/211) BbF BkF IP BaP BbF BkF IP Measured (16/1/212) BaP BbF BkF IP Computed (16/1/212) Significant spatial differences in the urban concentrations Significant seasonal differences (winter vs. summer) Overestimation of observed concentrations during colder periods BaP BbF BkF IP BaP BbF BkF IP

19 Limits and solutions undertaken

20 Correction factor Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May UTM-y [km] UTM-y [km] Computed UTM-y [km] Computed Limits and solutions undertaken Modelling problems and needs An overestimation of observed concentrations was identified during colder periods due to: the large uncertainty affecting PAHs emission estimates from the domestic heating sector; difficulties in distributing emissions within the urban texture at the chosen high resolution; A correction of modeled PAHs is needed! Monthly modelling/observations ratio correction factor (f c m): Integration measurements and modelling DATA FUSION C f Corrected m c m C Cm ( t) C Model m Observation Model m ( t) ( t) ( t) / f c m Measurements NO2 Measured NO Monthly correction profile UTM-x [km] Measured Validated Model UTM-x [km] BaP BbF BkF IP PAHS Successive Correction Method (SCM) (Bratseth, 1986) UTM-x [km]

21 [ng m -3 ] [ng m -3 ] FRA CGU BEL MAG FRA CGU BEL MAG [ng m -3 ] [ng m -3 ] FRA CGU BEL MAG FRA CGU BEL MAG Limits and solutions undertaken 12 Measured (12/12/211) Measured 12 Computed (12/12/211) 1% model-base case 1 8 BaP BbF BkF IP Data_Fusion (12/12/211) BaP BbF BkF IP BaP BbF BkF IP Data fusion Modelling/observations correction factor (f c m) fc(12/12/211) BaP BbF BkF IP Significant improvements of model predictions BaP BbF BkF IP

22 Limits and solutions undertaken 1% model Data fusion Model/obs. Correc. factor f c m

23 FRA [ng m -3 ] [ng m -3 ] FRA CGU BEL MAG FRA CGU BEL MAG FRA CGU BEL MAG [ng m -3 ] [ng m -3 ] FRA CGU BEL Limits and solutions undertaken Measured (14/5/212) Measured BaP BbF BkF IP Computed (14/5/212) 1% model-base case Data_Fusion BaP (14/5/212) fc(14/5/212) BbF BkF IP Data fusion Modelling/observations BaP BbF correction BkF IP factor (f c m) BaP BbF BkF IP BaP BbF BkF IP Reduced model overprediction - lower impact than winter months

24 Limits and solutions undertaken 1% model Data fusion Model/obs. Correc. factor f c m

25 (19) (5) (6) (3) (1) (6) (6) (3) (9) (6) (1) (3) (3) (3) (3) (3) (3) (3) (19) (5) (6) (3) (1) (6) (6) (3) (9) (6) (1) (3) (3) (3) (3) (3) (3) (3) (3) (2) FRA (2) (22) CGU (4) BEL (6) (6) MAG (4) (19) (5) (6) (3) (1) (6) (6) (3) (9) (6) (1) (3) (3) (3) (3) (3) (3) (3) (3) (2) FRA (2) (22) CGU (4) BEL (6) (6) MAG (4) Limits and solutions undertaken IOA - B[a]P BaP - IA Base Case Data Fusion Fc Gc Run Comparison of indexes of performances IOA (1) R (1) FAC2 (1 %) MFE ( %) RMSE ( ng m -3 ) B[a]P Base Case Data fusion IOA PAHs - IA - PAHs F c m PAHs Base Case Data fusion F c m Monitoring Net improvements of all indexes of performances

26 Limits and solutions undertaken 1% model Model/obs. Correc. factor f c m Legal limit of 1 ng/m 3 as year average not exceeded when correction is applied

27 Limits and solutions undertaken Heating (Nov-Mar) Non-Heating (May-Oct) Limit of 1 ng/m 3 exceeded during the heating season

28 Limits and solutions undertaken Modeled BaP/PAHs Population density map Cumulative population exposure

29 Conclusions PAHs modelling study was set up to estimate the exposure of population to PAHs in the city of Rome at high spatial resolution (1 km); ROME PAHs emission inventory: first experience of estimation of PAHs emission in a large conurbation using a bottom-up approach; biomass combustions/domestic heating is the main source; Uncertainties in available national and European PAHs emission inventories (activities and congeners) Additional information needed: questionnaire, fuels consumption; proxy for built areas; Modelling study exhibits: ability to catch the seasonal behaviour of PAHs in Rome; Overestimation was detected for the modelled results, especially during the colder seasons, due to the large uncertainty affecting PAHs emission estimates; Correction methods, based on both monthly averaged observed/modelled concentration ratios and data fusion technique, were tested; Results of indexes of performance indicate the effectiveness of these correction methods in producing PAHs concentrations in agreement with observations, allowing the use of corrected model results for an health effects assessment studies.

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