A source apportionment study and model validation for HMs and POPs air concentrations of MINNI project C. Silibello 1 , G. Calori 1 , M. Costa 1 , P. Radice 1 , M. Mircea 2. 1 ARIANET Srl, Via Gilino, 9, 20128, Milan, Italy 2 ENEA, National Agency for New Technologies, Energy and Sustainable - PowerPoint PPT Presentation
A source apportionment study and A source apportionment study and model validation for HMs and POPs air model validation for HMs and POPs air concentrations of MINNI project concentrations of MINNI project C. Silibello C. Silibello 1 , G. Calori , G. Calori 1 , M. Costa , M. Costa 1 , P. Radice , P. Radice 1 , , M. M. Mircea Mircea 2 2 13 th TFMM annual meeting: 17 th –19 th April 2012 - Grand Hotel – Mgarr – Gozo – MALTA 1 ARIANET Srl, Via Gilino, 9, 20128, Milan, Italy 2 ENEA, National Agency for New Technologies, Energy and Sustainable Economic Development, via Martiri di Monte Sole 4, 40129, Bologna, Italy
Transcript
A source apportionment study and A source apportionment study and model validation for HMs and POPs air model validation for HMs and POPs air
concentrations of MINNI projectconcentrations of MINNI project
C. SilibelloC. Silibello11, G. Calori, G. Calori11, M. Costa, M. Costa11, P. Radice, P. Radice11, , M. M. MirceaMircea22
13th TFMM annual meeting:17th–19th April 2012 - Grand Hotel – Mgarr – Gozo – MALTA
1 ARIANET Srl, Via Gilino, 9, 20128, Milan, Italy
2 ENEA, National Agency for New Technologies, Energy and Sustainable
Economic Development, via Martiri di Monte Sole 4, 40129, Bologna, Italy
Emission subsystemNational emission inventory for heavy
metals: year 2005
0%10%20%30%40%50%60%70%80%90%
100%
As Cd Cr Hg Ni Pb Cu Se Zn
Energy Production Comb in ResidentialComb.in Industry Prod. ProcessesRoad Transport Other Transport & Mobile MachineryWaste Treatment
(ISPRA, 2009)
Emissions of surrounding countries from EMEP
National emission inventoryby province and sector (NUTS3 and SNAP2/3) +
LPS (~140)
Emission subsystemIntegration of inventories
International (EMEP)National
Total
Emission subsystemExample: Sea traffic
EMEP area
Agreed area for Italy
EMEP MSC-E air concentrations 50 kmx50km, 6 hours
• HMs:•Pb, Cd, Hg, As, Ni, Cr, Zn, Cu, Se;
• POPs:•4 indicator PAHs;•γ-HCH;•HCB;•17 congeners of PCDD/Fs;
•5 congeners PCBs.
Boundary conditions: EMEP Meteorological Synthesizing Centre East (MSC-E)
EMEP50km x 50km
MINNI20km x 20km
Yearly average concentrations of As
Wind re-suspension from soil and Wind re-suspension from soil and seawaterseawater
Wind re-suspension from soil and seawater
Wind re-suspension of particles from soil and with sea-salt is estimated using Vautard et al. (2005) and Zhang et al. (2005). The production of dust from soils is not taken into account if precipitation during the last 48 hours exceeds 0.5 mm.
Emission factors of heavy metals for suspension with sea-salt aerosol
Metal Emission factor (mg/kg)
Reference
As 300 Nriagu, 1989
Cd 40 Richardson et al., 2001
Cr 80 Nriagu, 1989
Ni 180 Nriagu, 1989
Pb 4000 Richardson et al., 2001
From: MODELLING OF HEAVY METALS ATMOSPHERIC DISPERSION IN EUROPE by Oleg Travnikov and Ilia Ilyin; Meteorological Synthesizing Centre – East of EMEP
As concentration in topsoilSpatial distribution of heavy metal concentration in soil has obtained using data available from FOREGS web site.For Eastern Europe and Africa default concentration values based on the literature data were used (Table below).
Metal
Soil concentration,
mg/kgReference
As 5 Beyer & Cromartie, 1987
Cd 0.2 Nriagu, 1980aCr 50 Shacklette et al.,
1970Ni 15 Nriagu, 1980bPb 15 Reimann and
Cariat, 1998
Default concentrations of heavy metals in soil
All sources
Yearly averaged concentration of AsMINNI
Wind re-suspension from soil and seawater
The contribution from Aeolian resuspension is about 1%
Comparison with experimental dataComparison with experimental data
Vicenza - via TommaseoVicenza - via TommaseoVicenza - via TommaseoVicenza - via TommaseoVicenza - via TommaseoVicenza - via TommaseoVicenza - via TommaseoVicenza - via TommaseoVicenza - via Tommaseo
Borgaro T. - CadutiBorgaro T. - CadutiBorgaro T. - CadutiBorgaro T. - CadutiBorgaro T. - CadutiBorgaro T. - CadutiBorgaro T. - CadutiBorgaro T. - CadutiBorgaro T. - CadutiDruento - La MandriaDruento - La MandriaDruento - La MandriaDruento - La MandriaDruento - La MandriaDruento - La MandriaDruento - La MandriaDruento - La MandriaDruento - La Mandria
Treviso - via Lancieri di NovaraTreviso - via Lancieri di NovaraTreviso - via Lancieri di NovaraTreviso - via Lancieri di NovaraTreviso - via Lancieri di NovaraTreviso - via Lancieri di NovaraTreviso - via Lancieri di NovaraTreviso - via Lancieri di NovaraTreviso - via Lancieri di Novara
Feltre - via ColomboFeltre - via ColomboFeltre - via ColomboFeltre - via ColomboFeltre - via ColomboFeltre - via ColomboFeltre - via ColomboFeltre - via ColomboFeltre - via Colombo
Serravalle S. - SpinetoSerravalle S. - SpinetoSerravalle S. - SpinetoSerravalle S. - SpinetoSerravalle S. - SpinetoSerravalle S. - SpinetoSerravalle S. - SpinetoSerravalle S. - SpinetoSerravalle S. - Spineto
Sector contribution:• Emission scenario: decrease of sector emissions (-20%);• Two months run of the AMS (winter –January- and summer –July-);• Computation of the variation between reference and scenario averaged
concentrations (). The percentage contribution of a sector i is computed as: i / i N
Source apportionment
Foreign sources:
A) Zeroing BCs ( “Long-range contribution”);B) Zeroing both BCs and emissions in surrounding countries
For both cases:a. Two months run of the AMS (winter –January- and summer –July-);b. Computation of the variation between reference and ‘case run’
concentrations (). The percentage contribution of the ‘case run’ is computed as: 100* / Creference
Foreign sources contributions:Foreign sources contributions:Ni and B[a]PNi and B[a]P
0
0.1
0.5
1
2
5
10
20
[%]0.1
1
2
5
10
20
30
40
50
60
70
80
90
NiLong-range contribution (A)
Contribution % Variation i
NiLong-range contribution (A)
Contribution %
[ng/m3]
0
0.1
0.5
1
2
5
10
20
[%]0.1
1
2
5
10
20
30
40
50
60
70
80
90
0
0.1
0.5
1
2
5
10
20
[%]0.1
1
2
5
10
20
30
40
50
60
70
80
90
NiSurrounding countries emissions (B)
Contribution % Variation i
0
0.01
0.05
0.1
0.3
0.5
1
2
[%]0.1
1
2
5
10
20
30
40
50
60
70
80
90
B[a]PLong-range contribution (A)
Contribution % Variation i
0
0.01
0.05
0.1
0.3
0.5
1
3
[%]0.1
1
2
5
10
20
30
40
50
60
70
80
90
B[a]PSurrounding countries emissions (B)
Contribution % Variation i
Major sectors contribution on Major sectors contribution on concentrationsconcentrations
Sector contribution:• combustion in energy production and transformation (Sector 1);• non-industrial combustion, residential and commercial (Sector 2);• combustion in manufacturing industry (Sector 3);• industrial processes (Sector 4);• road transport (Sector 7);• other mobile sources (Sector 8);• waste treatment and disposal (Sector 9).
PbCombustion in Residential, Combustion in Industry and
Production Processes
0%10%20%30%40%50%60%70%80%90%
100%
As Cd Cr Hg Ni Pb Cu Se Zn
Energy Production Comb in ResidentialComb.in Industry Prod. ProcessesRoad Transport Other Transport & Mobile MachineryWaste Treatment
0
0.001
0.005
0.01
0.05
0.1
0.3
0.5
1
5
[% ]0.1
1
2
5
10
20
30
40
50
60
70
80
90
[ng/m 3]
PbNon-industrial combustion, residential and commercial (Sector 2)
Contribution % Variation i
0
0.001
0.005
0.01
0.05
0.1
0.3
0.5
1
5
[% ]0.1
1
2
5
10
20
30
40
50
60
70
80
90
[ng/m 3]
PbCombustion in manufacturing industry (Sector 3)
Contribution % Variation i
0
0.001
0.005
0.01
0.05
0.1
0.3
0.5
1
5
[% ]0.1
1
2
5
10
20
30
40
50
60
70
80
90
[ng/m 3]
PbProduction processes (Sector 4)
Contribution % Variation i
0
0.001
0.005
0.01
0.05
0.1
0.3
0.5
1
[% ]0.1
1
2
5
10
20
30
40
50
60
70
80
90
[ng/m 3]
B[a]PNon-industrial combustion, residential and commercial (Sector 2)
Contribution % Variation i
Summary and future plans the atmospheric modelling system of the MINNI project is able to simulate realistic
concentrations of heavy metals and B[a]P;
lower values of modelled concentrations suggest a significant underestimation in the emissions. A better knowledge in emission inventories may improve predictions;
more observations, for longer periods and covering the whole country, are necessary for a comprehensive validation of model results;
the contribution of Aeolian resuspension to heavy metals concentrations is low; B[a]P concentrations are strongly influenced by national sources, over most of the Italian territory;
the effect of foreign emissions is higher near the Alpine border at the north, over the islands (Sardinia and Sicily) and in some rural areas in the central-southern part of the Italian peninsula, far from densely inhabited zone;
civil heating, particularly in the regions where wood burning devices are used, is the main contributor B[a]P concentrations. The contribution of the industrial sector isrelevant around major facilities, with the largest absolute contribution in Taranto, whose steel industries are the largest individual source of PAH in the country according to the national emission inventory. Road traffic contributes for a few percentage points, with highest absolute contributions in the Po Valley and near Rome and Naples metropolitan areas. The remaining sectors play an almost negligible role.
simulations for other years, with higher spatial resolution, coupling of POPs partitioning to full chemical model (SAPRC99 and AERO3)
Acknowledgements
This work is part of the MINNI (Integrated National Model in support to the International Negotiation on Air Pollution) project, funded by the Italian Ministry of Environment, Territory and Sea.
We wish to thank Ilia Ilyin, Marina Varygina and Alexey Vladimirovich Gusev (EMEP MSC-E) and Anna Carlin Benedictow and Michael Gauss (EMEP MSC-W) for providing EMEP models output.
We also wish to thank Beatrice Bondanelli (Autonomous Province of Bolzano), Monica Angelucci (Environmental Agency of Umbria Region), Sandro Zampilloni (Lazio Region), Carla Contardi (Piemonte Region), Fulvio Stel (Environmental Agency of Region Friuli-Venezia Giulia), Giuseppe Onorati (Campania Region), Salvatore Patti (Environmental Agency of Veneto Region) for supplying monitoring data.
