Risks, challenges and mitigation actions in the APICE partners’ area: between the scientific findings and new governance models - Genoa

M.C. Bove, P. Brotto,F. Cassola, E. Cuccia, D. Massabò, A. Mazzino, P. Prati

Department of Physics – University of Genoa

Final conference – Venice, 8th November 2012

APICE scientific issues: the case of Genoa

Main goal: to provide Authorities and Stakeholders with a

reliable tool to study and forecast air quality: a “Chemical

Transport Model, CTM”

Methodology (shared with all the Partners):

1) “picture” of air quality (i.e. PM2.5) with a 1-year

monitoring campaign Source Apportionment (SA).

2) CTM assessment with updated emission data

3) Check of CTM vs. real-world measured data

4) Comparison of SA by CTM and monitoring campaign

2

Site1:C.So Firenze

Site2: Multedo

Site3: Bolzaneto

PORT

Monitoring campaign

Intensive campaign (May-Oct 2011)

after prevailing meteo conditons analysis

• The PM2.5 level is almost the same in the three sites

Main PM2.5 sources: at “regional” scale

PM2.5 levelsMass Concentration

y = 1.07x

R2 = 0.5

y = 0.98x

R2 = 0.7

0

5

10

15

20

25

30

35

0 5 10 15 20 25 30 35

C.so Firenze (mg/m3)

Mu

lte

do

an

d B

olz

an

eto

(m

g/m

3 )

Multedo

Bolzaneto

• The correlation between PM2.5 time series is stronger for the sites much closer to the port

F

M

B

PM2.5 average apportionment: Corso Firenze Corso Firenze

7±2%

23±3%

8±3%

14±5%49±5%

Oil combustion Soil Nitrates Traffic Sulphates

Corso Firenze

0%

20%

40%

60%

80%

100%

Al

Si K

Ca Ti V

Mn

Fe Ni

Cu

Zn

Pb

OC

EC

NO

3-

SO

4--

Na

NH

4+

Sulphates

Traffic

Oil combustion

Soil

Nitrates

(14 ± 5) %

Multedo

50±3%

5±3%

17±3%

8±2%

7±2%

12±4%

Oil combustion Soil Nitrates Traffic Sulphates Zn Mn

Multedo

0%

20%

40%

60%

80%

100%

Al

Si K

Ca Ti V

Mn

Fe Ni

Cu

Zn

Pb

OC

EC

NO

3-

SO

4--

Na

+

NH

4+

zn mn

Traffic

Sulphates

Soil

Nitrates

Oil combustion

PM2.5 average apportionment: Multedo

(12 ± 4) %

Bolzaneto

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Al

Si K

Ca Ti V

Mn

Fe Ni

Cu

Zn

Pb

OC

EC

NO

3-

SO

4--

Na

NH

4+

Traffic

Local

Sulphates

Fe Mn

Nitrates

Soil

Oil combustion

Bolzaneto

21±3%

5±2% 9±3%6±3%

8±2%

5±2%

46±3%

Oil combustion Soil Nitrates Traffic Sulphates Local source Fe Mn

PM2.5 average apportionment: Bolzaneto

(9 ± 3) %

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Oilcombustion

Soil Nitrates Traffi c Sulphates Localsource

Fe Mn Zn Mn

Conc

entr

ation

(ng/

m3 ) bolzaneto

multedo

corso fi

PM2.5 apportionment at a glance

Basically: ship emissions

Oil combustion

0

500

1000

1500

2000

2500

3000

June 2011 July 2011 August 2011 September 2011

Ave

rag

e P

M2.

5 (n

g/m

3 )

Bolzaneto

Corso Firenze

Multedo

Temporal behaviour of ship emissions

Many ferries to the

Islands

Meteorological preprocessor: WRF

3-domain configuration (10 km + 3.3 + 1.1 km)

Simulations driven by NCEP GFS fields (0.5°)

24-hr-long simulations, hourly outputs, year 2011

10

11

Chemical transport model: CAMx

• Maritime sector (harbour activities)

• Road transport

• Industry

• Non-industrial combustion plants

• Other sources (including natural emissions)

Outer domain covering Western and Central Europe (10 km resolution)

2-way nesting procedure

Inner domain – focus on local area

47x47 grid points

1.1 km resolution

PM source apportionment approaches:

Zero-out

CAMx PSAT routine

City area

Harbor area

Pollutants:

NOx, SOx, CO, PM….

Emission data

Large-scale anthropogenic emission data provided by AUTH (TNO data processed through the MOSESS code)

Natural emissions obtained processing WRF outputs with the NEMO code (developed by AUTH)

Updated (2010) harbour emission data calculated by Techne Srl (provider of Province of Genoa) according to CORINAIR Guidebook 2011 (no disaggregation for different harbour activities contribution available)

Local gridded emission data provided by Liguria Region (reference year 2008):

• 1 km spatial resolution• hourly temporal resolution• SNAP sectors disaggregation

12

Model validation – comparison with observed data (PM2.5)

13

Model validation – comparison with observed data (Sulfates)

14

Model validation – comparison with observed data (NOx)

15

16

CTM source apportionment results (zero-out)

PM2.5 NOx

Contribution restricted to the area around the harbour (expecially for PM2.5)

Contribution of harbour activities (%)

Summer 2011

17

CTM source apportionment results (zero-out)

PM2.5 NOx

Contribution of road transport (%)

Summer 2011

Contribution to concentrations over the whole city

18

SourcesSA by measured data

(PMF)SA by CTM

(CAMx with PSAT)

Maritime (13 ± 5) % coast (9 ± 3) % inland

9% coast 5% inland

Industrial(30 ± 10) % 20%

Road Traffic (40 ± 15) % 45%

Residential combustion Not resolved 5 %

Others (crustal, sea, etc. ) (15 ± 5) % 20%

SA of PM2.5: June- August 2011 - Intercomparison

± ???

19

Harbor activities contribution to PM2.5 concentrations CTM vs Receptor models

CTM Receptor Models

Cso Firenze

11 % (14 ± 5) %

Multedo 9 % (12 ± 4) %

Bolzaneto 4 % (9 ± 3) %

20

-20 %

+2 %

Future scenario analysis: PM2.5 Scenario 1 – 2020 without mitigation actions

21

- 35 %

- 5 %

Future scenario analysis: PM2.5 Scenario 2 – 2020 with S % reduction in fuels

22

- 40 %

- 5 %

Future scenario analysis: PM2.5 Scenario 3 – 2020 with S % reduction in fuels and

cold ironing of container and ferries terminal

23

Summary

A quite complete picture of PM2.5 levels and sources for the year 2011 has been obtained thanks to a considerable experimental effort

A CTM model has been implemented and put in operation: validation vs. measured data pretty good

Source apportionment by real-world data + receptor model (PMF) and CTM (WRF+CAMx) in fair agreement

Future scenarios according to stakeholders inputs and APICE methodology completed (reference year 2020)