Multiscale modelling system for pollutant concentration predictions in urban areas

Transcription

1 Multiscale modelling system for pollutant concentration predictions in urban areas Stefano Bande1, Massimo Muraro1, Matteo Giorcelli2, Roberta De Maria1, Monica Clemente1, Sandro Finardi2, Maria Grazia Morselli2 1 ARPA Piemonte, corso Unione Sovietica 216, Torino (TO), Italy; 2 ARIANET, via Gilino 9, Milano, Italy Workshop on Air Pollution in Urban areas Torino, 7 novembre 2007 Area Previsione e Monitoraggio Ambientale 1

2 OVERVIEW ARPA Piemonte, in cooperation with Arianet Consulting, has developed a multi-scale air quality forecasting system for Torino city and Novara Province. The system employs a background domain including the whole Piemonte Region (4 km horizontal resolution) and two high-resolution target domains (1km horizontal resolution) covering Torino metropolitan area and Novara Province. This multi-scale approach (nested grids) allows to take into account the effect of sources located outside the target areas and to better describe phenomena characterized by large spatial scales. The forecasting system has been running experimentally since the end of November 2005 and can be considered completely operative since July In this presentation we briefly describe: the modelling system architecture and main modules the modelling system performances the first implementation of the forecasted AIR QUALITY INDEX (IQA) for Torino urban area 2

3 MODELLING SYSTEM ARCHITECTURE 3

4 MODELLING SYSTEM COMPUTATIONAL DOMAINS Regional domain (g1) g1 g2 g2 xy (km) Nx Ny vertical levels (up to 3500 agl) Novara area (g2) Torino area (g2) 4

5 EMISSION pre-processing Gridded hourly emission rates are produced, for the regional domain and each high resolution domain, by EMMA module (Arianet) starting from Piemonte and nearby regions emission inventories. 5

6 EMISSION pre-processing : emissive dataset Emission data (point, line and area sources) come from different resolution inventories available over the area: detailed regional dataset for Piemonte, Lombardia and Valle d'aosta regions, Italian dataset for Liguria region and EMEP inventory for foreign countries. Point sources EMEP INEMAR Lombardia Valle d'aosta Regional Inventory NOx Area sources INEMAR Piemonte CORINAIR Italia NOx 6

7 METEOROLOGICAL pre-processing: meteorological input The meteorological fields are provided by the numerical weather forecast model COSMO-I7, the Italian version of COSMO-MODEL, a non-hydrostatic limited area model developed in the framework of the COSMO Consortium (COnsortium for Small-scale MOdelling, Operational implementation of COSMO-I7: ~7 km horizontal resolution, 40 vertical levels, two daily runs (12 and 00 UTC), lasting 72 hours initial and Boundary conditions by ECMWF (European Centre for Medium Range Weather Forecasts) COSMO-I7 meteorological fields used by the modelling system: 3D fields: wind, temperature, pressure and humidity; 25 vertical levels up to 8000 m, every 3 hours; 2D fields: total precipitation and cloud cover (total, low, medium and high); hourly data. 7

8 METEOROLOGICAL pre-processing COSMO meteorological fields are adapted to all computational domains through the interface module GAP/TINT (Arianet) carrying out spatial and temporal interpolation. Starting from high-resolution topography, land use data and downscaled meteorological fields, the meteorological processor SURFPRO (Arianet) computes turbulence scaling parameters fields, eddy diffusivities and deposition velocities, using parametrizations based on the surface energy balance and Monin-Obukhov theory. g2 Novara g1 Piemonte g2 Torino 8

9 THE AIR QUALITY MODEL FARM (Flexible Air quality Regional Model), eulerian chemical transport model developed by Arianet (derived from STEM-II) Diffuse sources and large point sources (LPS) with plume rise Actinic flux reduction effect from clouds SOx-NOx-NH3 simplified scheme (EMEP) Photochemistry: SAPRC-90 chemical scheme (reduced computational time) PM: aero0 simplified bulk module (reduced computational time) Two-way nesting Boundary and initial conditions built from CHIMERE continental forecasts (~50 km horizontal resolution) provided by the Prev'Air European scale air quality system ( Interpolation of CHIMERE fields into FARM grids, Gas-phase chemical mechanism conversion (MELCHIOR2--> SAPRC90), Aerosol aggregation into FARM aero0 classes 9

10 MODELLING SYSTEM SCHEDULATION Step1 input data processing Step2 air quality simulation (two parallel runs of FARM in two-way nesting mode) Forecast time: 48 hours (2 days forecasts, starting from today 01:00 a.m); Pollutants: SO2, NOx, NO2, CO, PM10, PM2.5, O3 and C6H6 Temporal resolution: 1 hour; Post-processing phase: maps, air quality indicators and Torino metropolitan area Air Quality Index (IQA) Forecast dissemination: forecast products are distributed to Torino and Novara Provinces, where they can be browsed and redistributed to end users and public through client applications ( 10

11 CONCENTRATION MAPS: PM10 g1 Piemonte g2 Torino g2 Novara 24/02/2007: PM10 [ g/m3] daily mean (run 23/02/2007) 11

12 MODELLING SYSTEM PERFORMANCES Evaluation period: 1 year (1/07/ /07/200 7) Pollutants: PM10, O3, NO2 Method: comparison between the observed data coming from the air quality monitoring network and the simulated ones at the corresponding station coordinates (bilinear interpolation starting from 4 nearest grid point values) 12

13 Comparison between +24 and +48 forecast Observed Predicted 1st forecast day (+24) TO-Consolata (Torino area urban traffic station) Observed Predicted 2nd forecast day (+48) PM10 hourly mean 01/12/ /12/2006 Piemonte g1 grid Spin-up problems during the first simulated hours: the first forecast day shows worse system performances than the second day (underestimation for NO2 and PM10 and overestimation for O3 concentration levels) Spatial resolution (~50 km) of the concentration fields provided by CHIMERE continental model is too low to give an accurate description of concentrations at regional scale and to define initial conditions This behaviour can be observed mainly at the urban stations 13

14 Modelling system performances: PM10 (+48 forecast, g2 grid) PM10 LVG weekly distribution TO Consolata (Torino area urban traffic station) Observed Predicted PM10 LVG monthly distribution TO Consolata (Torino area urban traffic station) Observed Predicted Seasonal and daily trends are accurately reproduced; Predicted median between 25th and 75th percentile of observed distribution in autumn and winter; Underestimation tendency during wintertime. 14

15 Modelling system performances: PM10 (+48 forecast, g2 grid) PM10 LVG daily averages TO Consolata (Torino area urban traffic station) PM10 LVG daily averages NO Leonardi (Novara area urban traffic station) forecasted PM10 daily averages within the accuracy required by EU and Italian legislations (dotted red lines) at almost station locations greater underestimation for stations located outside the Torino metropolitan area. 15

16 Modelling system performances: O3 daily maximum 8-hour running average (+48 forecast, g2 grid) NO Verdi - monthly distribution (Novara area urban background station) Observed forecasted/observed values - Druento La Mandria (Torino area rural background station) Predicted seasonal and daily trends are accurately reproduced; very good results: peak, median and hourly values are properly simulated both in urban and rural stations; 16

17 Modelling system performances: NO2 (+48 forecast, g2 grid) NO2 hourly mean monthly distribution - TO Rebaudengo (Torino area urban traffic station) Observed Predicted seasonal and daily trends are quite accurately reproduced; satisfactory results; NO2 hourly mean monthly distribution - NO Bovio (Novara area urban background station) Observed Predicted the model shows different behaviours: overestimation in the urban background stations and underestimation in urban traffic and suburban stations. 17

18 AIR QUALITY INDEX (IQA) for Torino urban area 18

19 OBSERVED AIR QUALITY INDEX (IQA) for Torino urban area Air quality information for Torino metropolitan area can be briefly and easily made available to the public by means of IQA. IQA value is daily computed according to the previous day air quality monitoring data, IQA is expressed by a numerical index ranging from 1 to 7; the higher is the value, the greater is the level of air pollution and the possible effects on health, IQA bulletin includes subjective evaluation of index tendency based on meteorological forecast, IQA takes into account ozone (O3) and PM10 during summer and PM10 and nitrogen dioxide (NO2) during winter. 19

20 FORECASTED AIR QUALITY INDEX for Torino urban area Step 1: area classification from land use data identification of the grid point inside the area of interest the code corresponding to land use class is assigned to each grid point Step 2: grid point concentrations simulated by the model PM10, NO2, O3 subindices calculation: Each grid point is linked to a specific weight depending on its code Each grid point concentration is normalised with the corresponding pollutant limit value each subindex is obtained by weighed average of grid point concentrations Finally IQA is calculated by averaging PM10 subindex and the highest between NO2 and O3 subindices 7 Step 3: Other infrastuctures Mining areas Argricultural and natural area Natural and residential areas Uncertain classification Residential and production areas Production areas Residential areas Farmstead Water and road body Technological areas 20

21 FORECASTED AIR QUALITY INDEX for Torino urban area preliminary results (july july 2007) IQA frequency distribution 01th July th July Predicetd Observed Satisfactory agreement Underestimation of IQA in higher observed values (mainly due to PM10 underestimation during winter season) 21

22 PRESENT/FUTURE WORKS Tests are on-going: to extend simulation time from +48 h to +72 h (three days) using COSMO-I7 00 UTC run; to get initial conditions from the previous day run to avoid spin-up problems and CHIMERE underestimation due to low resolution; Inprogress developments: to introduce a new background domain between CHIMERE continental domain and regional domains (new nesting level); to update and improve PM emission inventory (in cooperation with Regione Piemonte); to update the chemical mechanism (SAPRC90 -> SAPRC99) and use a more complete aerosol scheme (aero3); to investigate winter PM accumulation phenomena within the Po Valley and improve its modelling capability; 22

23 THANK YOU FOR YOUR ATTENTION! 23