Integrated Assesments Models for Air Pollutions

Integrated Assessment Models (IAMs)

for Air Pollution

Luisella Ciancarella [email protected]

Course of VIIAS Project Exposure Assessment in air pollution epidemiology and Health Impact Assessment

Roma, 9-13 December 2013

UTVALAMB-AIR Technical Unit for Models, Methods and Technologies for Environmental Assessment – Air Quality Laboratory

The DPSIR framework of European Environment Agency

DRIVERS

PRESSURES

RESPONSES

IMPACT

STATE

ie. industry, transports

ie. pollutant emissions es. health effects, loss of biodiversity economic damages

ie. Clean production public transport , incentives, taxes information

ie. air, water, soil quality

HEALTH BASED STANDARDS FOR AIR POLLUTANT CONCENTRATIONS

(D.Lgs. 13 agosto 2010, n. 155 “Implemetation of Directive 2008/50/CE on ambient air quality and cleaner air for Europe”)

Pollutant Concentration Averaging period Legal nature

Permitted

exceedences

each year

Fine particles (PM2.5) 25 µg/m3 1 year Target value entered into force 1.1.2010

Limit value enters into force 1.1.2015

n/a

Sulphur dioxide (SO2) 350 µg/m3 1 hour Limit value entered into force 1.1.2005 24

125 µg/m3 24 hours Limit value entered into force 1.1.2005 3

Nitrogen dioxide (NO2) 200 µg/m3 1 hour Limit value entered into force 1.1.2010 18

40 µg/m3 1 year Limit value entered into force 1.1.2010* n/a

PM10 50 µg/m3 24 hours Limit value entered into force 1.1.2005** 35

40 µg/m3 1 year Limit value entered into force 1.1.2005** n/a

Lead (Pb) 0.5 µg/m3 1 year Limit value entered into force 1.1.2005 (or

1.1.2010 in the immediate vicinity of

specific, notified industrial sources; and a

1.0 µg/m3 limit value applied from 1.1.2005

to 31.12.2009)

n/a

Carbon monoxide (CO) 10 mg/m3 Maximum daily 8 hour mean Limit value entered into force 1.1.2005 n/a

Benzene 5 µg/m3 1 year Limit value entered into force 1.1.2010** n/a

Ozone 120 µg/m3 Maximum daily 8 hour mean Target value entered into force 1.1.2010 25 days

averaged over 3

years

Arsenic (As) 6 ng/m3 1 year Target value enters into force 31.12.2012 n/a

Cadmium (Cd) 5 ng/m3 1 year Target value enters into force 31.12.2012 n/a

Nickel (Ni) 20 ng/m3 1 year Target value enters into force 31.12.2012 n/a

Polycyclic Aromatic

Hydrocarbons

1 ng/m3

(expressed as concentration of

Benzo(a)pyrene)

1 year Target value enters into force 31.12.2012 n/a

POLLUTANTS ARE NOT ALL THE SAME

emitted directly from sources

produced from chemical reactions starting from the primary pollutants

O3 – OZONE a secondary pollutant

NO2

NO

O2

O3

UV

Radiations

O•

O•

COV reactive organic

NO2

PM- Particulate Matter

Semi-volatile

Organic Vapors

Gas phase

photochemistry

H2SO4

Primary Organic

PM emissions

(OC, EC)

Primary Inorganic

PM emissions

(dust, fly ash, ecc.)

Sea salt

NH3 Emissions

H2O

H2SO4 Primary

Emissions

Primary organic

gasesSO2 Emissions

HNO3

NOX Emissions

Gas phase

photochemistry

Gas Phase

Photochemistry

Incorporation paths of chemical species in atmospheric particulate matter

This is a problem ……….

In order to avoid, prevent or reduce harmful effects on human health and the environment we must aim to air quality objectives (=> concentrations) We can only work on “pressures” (emissions) starting from human activities that determine them (driving forces) We are not sure of the “impact” we produce changing the “pressures” because the atmospheric system is not linear and because there are also cross-border contributions (*)

(*) Much of the sulfur (> 70%), nitrogen oxides (> 70%) and ammonia (45%) emitted in Italy travels across national borders, going to deposit beyond our borders

By contrast, 58% sulfur, 30% of oxides of nitrogen and 12% of the ammonia that interact on our territory come from other countries

THE IAM APPLIED TO AIR POLLUTION

Simplified flow chart

Emissions Dispersion in

the atmosphere

Depositions &

concentrations

Environmental

and Health

effects

Control

Strategy

Implementation

costs

Social aspects GDP/Costs

per capita

Atmospheric Transfer Matrices

Economic

growth

Human

Activities

Energy/agricultural projections

Emissions

Emission control options

Atmospheric dispersion

Health and environmental impacts

Costs

Environmental targets

Driving forces

OPTIMIZATION

International Institute for Applied Systems Analysis http://gains.iiasa.ac.at/index.php/home-page

Integrated Assessment Model (IAM) approach

GAINS-Italy as GAINS-Europe

PM SO2 NOx VOC NH3 CO2 CH4 N2O CFCs HFCs SF6

Health impacts: PM

O3

Vegetation damage: O3

Acidification

Eutrophication

Radiative forcing: - direct

- via aerosols

- via OH

GAINS - Greenhouse Gas and Air Pollution

Interactions and Synergies

Economic synergies between emission control measures

Mu

ltip

le b

en

efits

Physical interactions

The multi-pollutant/ multi-effect approach extended to greenhouse gases

This implies synergies but also trade-offs . . .

Examples:

3-way catalysts: NOx (↓) , PM (↓), but N2O (↑), NH3 (↑)

Switch to gas as fuel: CO2 (↓), ma CH4 (↑) (ceteris paribus) from transport and distribution

Gas flaring: CH4 (↓), but CO2 (↑), NOx (↑)

Carbon capture and storage: CO2 (↓), but fuel use (↑)

Pellet Stoves: NOx (↓) , PM (↓)

Waste Incineration: CH4 (↓), other fuels demand (↓), but CO2 (↑)

Reducing the use of fertilizers: N2O (↓), energy (↓)

…

Scenario simulations with GAINS Italy http://gains-it.bologna.enea.it/gains/IT/index.login

Scenario simulations with GAINS -Italy

ACTIVITY LEVELS SCENARIOS

Energy/Agricultural Projections…

Driving forces

ENERGY SCENARIO

PRODUCTION ACTIVITIES SCENARIO

1

WHAT A SCENARIO IS ….

A SCENARIO is: a picture of the future a trajectory in the space of the possible events ... Whatever the definition, the common element is that the processing is based on

scientific criteria plausible hypotheses; internal consistency (consistency of the values assumed by the different

variables); transparency (reproducibility of each scenario). A scenario is not a forecast, but a complete and coherent

representation of one possible future given certain assumptions and using a given methodology

ENERGY SCENARIO

Energy Projections…

Driving forces 1a

In Italy the activities for the development of national energy scenarios are carried out by a working group that includes the Ministry for Economic Development, the Ministry of Environment, ISPRA (Institute for the Protection and Environmental Research) and ENEA, using a "bottom up" techno-economic model implemented on software Markal.

For the European Union similar scenarios are developed with the

PRIMES model

The more recent Energy Scenario driver in GAINS-Italy is based on the National Energy Strategy SEN approved in July 2013

GAINS-ITALY INPUT: ENERGY SCENARIO

Examples of fuels considered in GAINS Italy

PRODUCTION ACTIVITIES SCENARIO

A statistical model is used to update the livestock numbers

projections

The forecasts for the consumption of nitrogen fertilizers are

based on literature (source: EFMA-European Fertilizer

Manufacturers Association)

The future scenarios of industrial processes or activities using

solvents are based on industry and trade associations

forecasts

Economic activities Projections…

Driving forces 1b

GAINS-ITALY INPUT: PRODUCTION SECTOR PROCESSES

GAINS-ITALY INPUT: AGRICULTURE ACTIVITIES

Emissions in Inventories

• Point sources: emissions are directly reported with reference to individual companies communications or measurements

• Linear and areal sources: emissions are estimated on

territorial basis according to the formula:

E / anno = A x EF where: E is the pollutant emission (ie. tons/year) A is the activity level (energy consumptions etc.) EF is the Emission Factor per activity level unit and for the specific pollutant

Emissions in GAINS-Italia

E = Σj Σk Actj * Efj * (1 – ŋjk) * Afjk

Actj = Activity Level in sector J

Efj = Unabated Emission Factor in sector J

(1 – ŋjk) * Afjk = Control of technology K in sector J

Emissions in Inventories

Ej = Actj * EFj

EFj = Total Emission Factor (control included) in sector J

Penetration of technology in the sector

Removal efficiency of the technology

GAINS INPUTS WHICH CAN BE PROJECTED IN THE FUTURE ….

Production activities

scenario

Energy Scenario GAINS-Italy

Control Strategy

(Control Technologies)

Input


Driving forces 3


GAINS-ITALY INPUT : THE CONTROL STRATEGY

TOTAL = 100 %

Some control technologies considered in the model

Examples of control technologies, and relative removal efficiency, for PM10 emissions from power plants and industrial processes

CONTROL TECHNOLOGIES GAINS MODEL

REMOVAL

EFFICIENCY(%)

PM10 PM2,5

Cyclone CYC 31:66 30,00

Electrostatic precipitator: 1 field ESP1 >96,00 93,00

Electrostatic precipitator: 2 fields ESP2 >99,99 96,00

High efficiency deduster HED >99,99 99,50

Fabric filters FF >99,99 99,50

Good housekeeping (industrial oil boilers GHIND 30,00 30,00

Good practice: ind.process-fugitive - stage 1 PRF_GP1 40,00 40,00

Good practice: ind.process-fugitive - stage 2 PRF_GP2 80,00 80,00

http://eippcb.jrc.es/

Best available techniques REFerence documents (BREFs)

CONTROL STRATEGIES FOR INDUSTRIAL ACTIVITIES/ 1

Guidelines for Best Available Technologies BAT

http://aia.minambiente.it/documentazione.aspx

http://ipts.jrc.ec.europa.eu/

http://www.europa.eu/

EMISSION

SCENARIOS IEA

VIA..

VAS..

AIR

QUALITY

SCENARIOS

The Integrated Environmental Authorization (IEA) è the provision that authorizes the operation of a plant, or of a part of it, under certain conditions which must ensure compliance with legal requirements as provide for in Directive 96/61/EC concerning integrated pollution prevention and control (IPPC)

THE PROGRESSIVE DEVELOPMENT OF INTEGRATION

GAINS OUTPUTS

GAINS-Italia

Emission Scenarios Costs curves Deposition maps Concentration maps Enviromental and Health Impact

Emissions Costs


Driving forces 4


The NOx emission scenarios

0

200

400

600

800

1000

1200

1400

2005 2010 2015 2020 2025 2030

NO

X e

mis

sio

ns

(kt)

Scenario comparison: total NOX emissions

TSAP_Apr2013

RUN2020_lug2013

SEN_set2013

NOCP_2010

0

200

400

600

800

1000

1200

1400

2005 2010 2015 2020 2025 2030

Emis

sio

ni N

OX

(kt)

NOX emissions Scenario- SEN 2013- sett 2013 - ITALY

Power Plants Raffinerie IndustriaCivile Trasporto su strada Trasporto off-roadTrasporto marittimo Rifiuti Nec target 2010

Scenario Emissioni NOX - ITALIA

NO2 Workshop, Aprile 2010 Bruxelles





The PM10 emission scenarios

0

50

100

150

200

250

2005 2010 2015 2020 2025 2030

PM

10

em

issi

on

s (k

t)

Scenario comparison: total PM10 emissions

TSAP_Apr2013

RUN2020_lug2013

SEN_set2013

NOCP_2010

0

30

60

90

120

150

180

210

2005 2010 2015 2020 2025 2030

Emis

sio

ni

PM

10

(kt

)

Scenario emissivo PM10 - SEN 2013 - versione sett 2013 - ITALIA

Power Plants Raffinerie Industria Civile

Trasporto su strada Trasporto off-road Trasporto marittimo Allevamenti

The PM2,5 emission scenarios

0

20

40

60

80

100

120

140

160

180

200

2005 2010 2015 2020 2025 2030

PM

2.5

em

issi

on

s (k

t)

Scenario comparison: total PM2.5 emissions

TSAP_Apr2013

RUN2020_lug2013

SEN_set2013

NOCP_2010

0

30

60

90

120

150

2005 2010 2015 2020 2025 2030

Emis

sio

ni

PM

2.5

(kt

)

PM2.5 scenario emission - SEN 2013 - sett 2013 - ITALY

Power Plants Raffinerie Industria Civile Trasporto su strada

Trasporto off-road Trasporto marittimo Allevamenti Rifiuti Altro

REALIGNMENT OF THE HISTORICAL EMISSION SERIES

INVENTORY EMISSIONS VS GAINS-ITALY EMISSIONS

GAINS IS AN EMISSION MODEL AND NOT AN INVENTORY

WHY THE PROJECTIONS ARE SOLID AND SHARED MUST BE

IDENTIFIED A BASE YEAR

IN THE BASE YEAR A CALIBRATION OF GAINS MUST BE

CARRIED OUT TO REPRODUCE IN OUTPUT THE NATIONAL

INVENTARY OF EMISSIONS

Harmonization

HARMONIZATION PROCESS

The process is applied in those sectors where major differences

between emissions are detected until you get a gap acceptable

(<5~6% on total)

Equivalence of Activity Levels

Compatibility of Total Emission

Factors

Unabated Emission

Factors modification

Check Emission Consistency

Control Strategy

modification

(technologies penetration)

From emission scenarios to concentrations scenarios in GAINS-Italy

Emissions Costs


Driving forces 4


Atmospheric dispersion

AQ IMPACT INDICATORS

GAINS indicators: O3 (SOMO35, AOT40), PM2.5/PM10, S (oxidized sulfur), N (oxidized nitrogen), NH (reduced nitrogen)

Precursors emissions: anthropogenic NOx, SO2, NH3, VOCs and primary PM10

Precursors emissions

GA

INS

in

dic

ato

rs

MINNI Integrated Assessment Model

Atmospheric Transfer Matrices

Emissions Projection (RAIL)

AMS: Atmospheric

Modelling System

AMS-Italy GAINS-Italy

GAINS: Greenhouse Gas and Air

Pollution Interactions and

Synergies

What are the ATMs?

ATMs allow to estimate how the changes in emission scenarios can affect pollutants concentrations and ground depositions.

They are “source to receptor” relationships, expressing the variations of depositions and concentrations (GAINS indicators) in each point of the domain as a response to variations of precursor emissions for given sets of aggregated sources.

They are an approximation of the response of the atmospheric system in a neighborhood of the “reference emission scenario”.

What the ATMs aim to?

The relationship between atmospheric emissions and pollutants concentrations and depositions is a key component in the policy evaluation process.

In fact, the direct use of complete air quality chemical models (AQMs) may become impractical for quick scenarios screening or for optimization, typically requiring many iterative calculations.

The ATMs approximate method is justified when it is necessary to get near real-time feedback on multiple scenarios analyses, where yearly runs of an AQM would require impractical computational resources.

Approximation of atmospheric system non-linear behavior:

• Contributes to depositions: we can add them only in conditions

similar to those of reference scenario

• Emission changes: not beyond the limits tested

• Dependence from the meteorologic year

Non-linearity:

• Answer to large changes of a precursor in a given set of emission sources

• Cross-effects (inside a set of pollutants and emission sources)

Meteorologic reference year: AVERAGE OF 4 YEARS 1999, 2003, 2005, 2007

Emission reference year : a scenario year (2015)

Considered precursors : anthropogenic SOx, NOx, NH3, NMVOC, PM10

Regional reductions : -25%

C

E

The “new” answers to ATM limits

Set of sources: the 20 Italian administrative regions

Computational domain: Italy (IT) with resolution 20x20 km and 16 vertical levels up to 10 km

Reference meteorological years: the ones considered up to now during the MINNI project:

1999, 2003, 2005 and 2007

FARM air quality model (ARIANET s.r.l., Milan), currently with SAPRC90+aero3 chemical/aerosol mechanisms.

METHODOLOGY 1

METHODOLOGY 2

1. First, we fix both reference emission and meteorological scenarios and run the AMS

2. The emission scenario is then altered, by selectively reducing the emissions of the five precursors in all the twenty regions by -25% and the AMS model is run again. So we have to perform 100 (20x5) AMS runs

3. Finally, starting from the GAINS indicator variations with respect to every single precursor emission change in each region, we determine their incremental ratios (linear approximation)

SOURCE-RECEPTOR RELATIONSHIPS

OZONE

i = set of emission sources (regions)

j = set of receptors (grid cells)

O j = ozone indicator (SOMO35 / AOT40F / AOT40C) at receptor point j

N i = NOx anthropogenic emissions in region i

V i = NMVOCs anthropogenic emissions in region i

tonij , to

vij = linear transfer coefficients for nitrogen oxides and NMVOC

koj = constant to fit the linear approximation to the reference case

i

jn

ijN

Oto

i

jv

ijV

Oto

ji

Ii

v

iji

Ii

n

ijj koVtoNtoO

PM

i = set of emission sources (regions)

j = set of receptors (grid cells)

PM2.5 j = annual mean concentration of PM2.5 at receptor point j

Pi = anthropogenic emissions of primary PM2.5 in region i

Si = SO2 anthropogenic emissions in region i

Ni = NOx anthropogenic emissions in region i

Ai = NH3 anthropogenic emissions in region i

αS,Wij, ν

S,W, σW,Aij, π

Aij = linear transfer coefficients for reduced (α) and oxidized (ν) nitrogen, sulfur (σ) and primary

PM2.5 (π) calculated for the following periods: winter (W), summer (S) and annual (A)

c1j , c2j = scaling factor from mol unit to μg/m3 (including water)

k1j , k2j = constants to fit NH4 and NO3 into reference case

k3j = make sure function fits reference case

jji

Ii

W

ijjji

Ii

W

ijji

Ii

W

ijj

i

Ii

S

iji

Ii

S

iji

Ii

A

ij

Ii

i

A

ijj

kkNckScAc

NASPPM

322,132

1411,0maxmin5.0

5.05.2

summer (may-october)

winter

SOURCE-RECEPTOR RELATIONSHIPS

Some preliminary AQM simulations were conducted in order to investigate the primary functional dependencies of the GAINS indicators from the precursors, with particular attention paid to detect the linearity degree in the considered range of emission variations (around -25%, and down to -50%).

The tests revealed a very good overall linearity: exceptions are the ozone indicators over main urban areas, which result depending on NOX in a slightly non-linear way.

PRELIMINARY TESTS

PRELIMINARY TESTS

Precursors

SO2 NOX PM10 NH3 VOCs

GA

INS

in

dic

ato

rs

S linear negligible negligible negligible negligible

N negligible linear negligible

anti correlated

accounts for

30%

quasi-linear

negligible

NH negligible negligible negligible linear negligible

O3

(SOMO35/AOT40) no

slightly

non-linear no no linear

PM10

linear,

secondary with

respect to

PM10

linear,

secondary with

respect to

PM10

linear,

leading

linear,

secondary with

respect to

PM10

linear,

secondary with

respect to

PM10

DEPENDENCIES AND LINEARITY DEGREE

CONTROL RUNS

AMS runs for selected scenarios against which to test the goodness of ATMs approximation

Changes in annual regional emissions compared to the reference scenario “GAINS no CP 2015”

“GAINS no-CP 2020” “Minus 25”

SO2 NOx NH3 NMVOC PM10

Abruzzo 5.5% -21.2% -6.1% -7.1% -3.8%

Basilicata -2.2% -18.4% -5.9% -2.3% -2.4%

Calabria -3.6% -19.0% -6.0% -4.2% -4.2%

Campania 5.3% -20.1% -7.9% -7.9% -4.3%

Emilia-Romagna -0.7% -19.8% -5.5% -7.4% -4.4%

Friuli - Venezia Giulia -1.1% -19.0% -9.5% -5.4% -4.0%

Lazio -4.3% -23.9% -8.0% -7.4% -4.9%

Liguria -1.4% -15.6% -12.0% -6.5% -1.9%

Lombardia -9.6% -17.0% -5.2% -5.2% -3.8%

Marche -0.3% -23.8% -10.0% -5.3% -4.5%

Molise -0.9% -16.5% -4.8% -2.9% 0.5%

Piemonte -4.9% -17.5% -5.8% 1.1% -2.4%

Puglia -2.0% -13.4% -13.8% -6.0% -3.7%

Sardegna -4.7% -13.2% -4.3% -3.6% -3.3%

Sicilia -1.3% -13.3% -8.2% -7.9% -4.4%

Trentino - Alto Adige -19.2% -26.5% -4.2% -3.4% -4.5%

Toscana 0.5% -15.3% -10.3% -5.2% -1.7%

Umbria -7.5% -17.5% -10.0% -7.0% -2.8%

Valle d'Aosta -14.4% -18.1% -3.9% -2.4% 0.8%

Veneto -0.9% -15.1% -7.5% -3.6% -1.3%

SO2 NOx NH3 NMVOC PM10

Abruzzo -25.0% -30.0% -25.0% -25.0% -25.0%

Basilicata -25.0% -30.0% -25.0% -25.0% -25.0%

Calabria -25.0% -30.0% -25.0% -25.0% -25.0%

Campania -25.0% -30.0% -25.0% -25.0% -25.0%

Emilia-Romagna -25.0% -30.0% -25.0% -25.0% -25.0%

Friuli - Venezia Giulia -25.0% -30.0% -25.0% -25.0% -25.0%

Lazio -25.0% -30.0% -25.0% -25.0% -25.0%

Liguria -25.0% -30.0% -25.0% -25.0% -25.0%

Lombardia -25.0% -30.0% -25.0% -25.0% -25.0%

Marche -25.0% -30.0% -25.0% -25.0% -25.0%

Molise -25.0% -30.0% -25.0% -25.0% -25.0%

Piemonte -25.0% -30.0% -25.0% -25.0% -25.0%

Puglia -25.0% -30.0% -25.0% -25.0% -25.0%

Sardegna -25.0% -30.0% -25.0% -25.0% -25.0%

Sicilia -25.0% -30.0% -25.0% -25.0% -25.0%

Trentino - Alto Adige -25.0% -30.0% -25.0% -25.0% -25.0%

Toscana -25.0% -30.0% -25.0% -25.0% -25.0%

Umbria -25.0% -30.0% -25.0% -25.0% -25.0%

Valle d'Aosta -25.0% -30.0% -25.0% -25.0% -25.0%

Veneto -25.0% -30.0% -25.0% -25.0% -25.0%

Control run “GAINS no CP 2020” Model run vs ATMs method

-2

-1.6

-1.2

-0.8

-0.4

0

0.4

0.8

1.2

1.6

2

%

-50

-40

-30

-20

-10

0

10

20

30

40

50

mg/m2/y

Differences ATM-model

S deposition

Absolute error Relative error

-12

-9

-7

-5

-3

-1

1

3

5

7

9

12

%

-500

-400

-300

-200

-100

0

100

200

300

400

500

mg/m2/y

N deposition




-20

-16

-12

-8

-4

0

4

8

12

16

20

%

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

ug/m3

PM2.5




NO2



-10

-8

-6

-4

-2

0

2

4

6

8

10

%

-1.5

-1.2

-0.9

-0.6

-0.3

0

0.3

0.6

0.9

1.2

1.5

ug/m3


-15

-12

-9

-6

-3

0

3

6

9

12

15

%

-500

-400

-300

-200

-100

0

100

200

300

400

500

ug/m3*h

SOMO35




Control run “minus 25” Model run vs ATMs method

-2

-1.6

-1.2

-0.8

-0.4

0

0.4

0.8

1.2

1.6

2

%

-50

-40

-30

-20

-10

0

10

20

30

40

50

mg/m2/y

S deposition

Differences ATM-model Absolute error Relative error

-12

-9

-7

-5

-3

-1

1

3

5

7

9

12

%

-500

-400

-300

-200

-100

0

100

200

300

400

500

mg/m2/y

N deposition




-20

-16

-12

-8

-4

0

4

8

12

16

20

%

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

ug/m3

PM2.5




NO2



-10

-8

-6

-4

-2

0

2

4

6

8

10

%

-1.5

-1.2

-0.9

-0.6

-0.3

0

0.3

0.6

0.9

1.2

1.5

ug/m3


-15

-12

-9

-6

-3

0

3

6

9

12

15

%

-500

-400

-300

-200

-100

0

100

200

300

400

500

ug/m3*h

SOMO35




REMARKS UPON CONTROL RUNS

Control runs have shown:

• “GAINS no-CP 2020”: overall good agreement (within about 5%)

• “minus 25”: upper range, agreement still acceptable (within 10%)

Exceptions are the ozone indicators, especially in the main metropolitan areas of Milan, Rome and Naples, where the ATMs method heavily underestimates. This confirms what we already saw in the preliminary tests

We need to take into account higher order terms

Ozone and 2nd order coefficients

You may think the GAINS indicators as functions of regional emissions…

i,k I = set of emission sources (regions) α = set of receptors (grid cells) P,Q P = precursors Cα = GAINS indicator on the receptor α EP

i = P precursor primary emission in region i tP

iα = linear transfer matrices S0 = reference scenario δ = constant to fit the linear approximation to the reference case

Number of runs to calculate linear terms: 5 x 20 + 1 =101 Number of runs needed to calculate 2nd order terms:

2 precursors and 20 regions 840 additional simulations

)(5.0)0( 3

,,

2

0

EOEEEE

CEtSCC

E

Ct

E

Ct

QPki

Q

k

P

iQ

k

P

iPi

P

i

P

i

P

i

P

i

S

P

i

P

i

P

I

P

I

2EEC guess

linearization

linear ATMs

OZONE AND 2ND ORDER COEFFICIENTS

A systematic determination of all the 2nd order terms is not straightforward, because of their too huge number. We have to give up this way. We need a good idea to overcome this problem!

Hypothesis: non linear contributions over the three areas depends mainly on local emissions, i.e. terms like

This means no regional cross terms, but precursor cross-

terms only, like: NOX ∙ NOX (N2)

VOC ∙ NOX (N∙V)

VOC ∙ VOC (V2)

Remember: the preliminary tests showed that ozone depends from VOCs in a linear way

liEE Q

l

P

i

We then checked such a formula:

Two test runs, performed by decreasing simultaneously VOC and NOX emissions by -25% and NOX by -50% in one of the three region (Lombardy), showed a negligible contribution of 2nd order cross terms VOC ∙ NOX:

This suggested to introduce second order terms determined by means of three additional runs only, performed by decreasing NOX emissions by -50% in the three regions, in order to calculate the term NOX ∙ NOX :

VVNNNO 2

VNNO 2

VN


-15

-12

-9

-6

-3

0

3

6

9

12

15

%

-15

-12

-9

-6

-3

0

3

6

9

12

15

%

SOMO35

SOMO35 = N + V + SOMO35 = N N2 + V +

“GAINS no CP 2020”


Ozone and 2nd order coefficients

SOMO35 = N + V + SOMO35 = N N2 + V +

SOMO35

“Minus 25”

-15

-12

-9

-6

-3

0

3

6

9

12

15

%

-15

-12

-9

-6

-3

0

3

6

9

12

15

%

THE “AVERAGE MATRIX”

Concentrations and depositions, and ATMs, show strong interannual variability, especially for what concerns ozone concentrations and S/N/NH depositions

O3 is influenced by 2003 and 2005 thermal anomalies

Deposition patterns are strongly correlated with the rainfall distribution

PM instead shows a smaller variability than ozone and depositions, but still noticeable.

We have four ATMs, determined on the basis of the selected reference emission scenario, for the meteorological years: 1999, 2003, 2005, 2007.

For each of the four yearly ATMs, the target is to evaluate the modifications in the averaged concentrations and depositions induced by regional precursor variations

GAINS indicators have now to be averaged over the whole meteorological period, that is the four years.

HOW DO WE COMPUTE IT?


HOW DO WE COMPUTE IT?


Meteorology-averaged ATMs have been so estimated by firstly averaging the concentration and deposition fields calculated over multiple (four) meteorological years and then applying the prescriptions we have seen before for the calculation of the linear yearly coefficients.

It is worth pointing out that averaging the ATM coefficients obtained from different meteorological years do not give the same result and it is conceptually wrong, because in order to fit the GAINS purpose we have to obtain the fields averaged over the ensemble.

SOME FINAL REMARKS

All the very complex indicator-precursors interdependencies are modeled in GAINS by means of ATMs, computed on the basis of complete 3D AMS simulations

ATMs are no suitable to deal with “local” measures, i.e. cases of reduction of some specific big industrial plant emissions. The GAINS-Italy resolution is aimed to take into account regional scale changes

However the variations of precursors are not uniformly distributed over the regions, but they follow the same spatial and temporal distributions as the reference scenario ones

For local measures and when emissions changes are outside the acceptable limits, a direct simulation with the AMS is needed

WHO IS RUNNING GAINS-IT AND FOR WHAT

At national level GAINS-IT is used as a tool to support policy makers in the negotiation processes for EU Directives and Policies

1. The revision of the Göteborg Protocol

2. The revision of the Thematic Strategy on Air Pollution

The use of the GAINS-It model allowed Italy to carefully investigate all emissions sector by sector and to provide

to the COMM a reliable national emission scenario.

… the final agreement on the Göteborg Protocol

Initial COMM proposal

(nov 2011)

COMM proposal

(feb 2012)

IT Ceilings in the GP (may

2012)

SO2 -38% / -42% -35% -35%

NOX -43% / -46% -40% -40%

PM2.5 -34% / -45% -17% -10%

NH3 -5% -9% -5%

VOC -48% / -56% -35% -35%

Pollutant

% Reduction at 2020 from 2005 level

The revision of the Göteborg Protocol..

- The bilateral meeting with IIASA, in order to define control strategies, emission factors and to harmonize data with the national emission inventory was a good chance to discuss and understand all the data behind the scenario elaborated by IIASA

- Many differences were observed at the year 2005 in emission estimations due to differences in fuel allocation, emission factors, control strategies, S content, biomass consumption, share of fuelwood in domestic technologies (stoves, fireplaces….)

- Total fuel consumption is often comparable but the allocation in the PRIMES scenario is not reliable especially in road transport and liquid fuels in industry, power plants and conversion sectors

- These discrepancies will lead to a different emission starting point at 2020 and will influence the following cost analysis with the risk that in the optimization process the most polluting sectors could not be considered

THE REVISION OF THE EU THEMATIC STRATEGY ON AIR POLLUTION

WHO IS RUNNING GAINS-IT AND FOR WHAT

At regional level GAINS-IT support the Regional Authorities responsible for AQ management

1. CLE Emission Scenarios

2. Support to formulate Local Plan Scenarios and to assess the impact of reduction measures

3. Training on the use of GAINS-IT on the web

THE ASSESSMENT OF TECHNICAL AND NON-TECHNICAL MEASURES IN THE REGIONAL PLANS FOR AIR QUALITY MANAGEMENT (2005-2008 Regional Plans)

Control Strategy definition

Technical Measures

GAINS-Italy

Emission inventory

Harmonization with national/local

emission inventories

Activity input data scenario

INPUT SCENARIO DEFINITION

(CLE, MTFR...)

Non Technical Measures

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

Measures

n°r

eg

ion

s

Energy, Domestic and Road Transport regional measure adoption frequency

1 = Urban Waste incineration with heat recovery; 2 = Biogas recovery in agricultural and in farming sectors; 3 = District heating Plant with waste and biomass; 4 = Photovoltaic; 5 = Wind; 6 = Hydroelectric; 7 = Geothermic Well; 8 = High efficiency domestic boilers; 9 = Energy efficiency in building; 10 = Residential heating accountability; 11 = Heat pumps;

12 = Solar heating systems; 13 = Regulation of some fuel use; 14 = Incentives for shift to natural gas in domestic boilers; 15 = Efficiency improvements in fireplaces and stoves; 16 = Low emission zones; 17 = Road traffic restriction; 18 = Pollution charge; 19 = Car sharing; 20 = Motorway speed limits; 21 = Bike sharing; 22 = Incentives for new cars;

23 = Incentives for new diesel heavy duty; 24 = Opening new rail lines; 25 = Opening new underground lines; 26 = Cycle paths; 27 = Sea motorway; 28 = Bus investment (new buses, service extension, frequency increase); 29 = Antiparticulate filter; 30 = Incentives for biofuel public transport; 31 = New methane service stations; 32 = Incentive for hydrogen cars; 33 = Rationalising load transport in urban area;

THE ASSESSMENT OF TECHNICAL AND NON-TECHNICAL MEASURES IN THE REGIONAL PLANS FOR AIR QUALITY MANAGEMENT (2005-2008 Regional Plans)

-35.00%

-30.00%

-25.00%

-20.00%

-15.00%

-10.00%

-5.00%

0.00%

5.00%

10.00%

15.00%

20.00%

SO2 NOx PM10 SO2 NOx PM10 SO2 NOx PM10 SO2 NOx PM10 SO2 NOx PM10 SO2 NOx PM10

Urban Waste

incineration with heat

recovery

District Heating Plant

with waste and

biomass

High efficiency

domestic boilers

Energy efficiency in

building Low emission zones

Incentives for new

cars

SO2, NOx, PM10 emission saved (%) on total sectoral regional emission calculated respect to the CLE scenario at 2010

The additive bars show the different sectoral emission

reduction for each Region where the AQ measures were applied

-8.00%

-6.00%

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

SO2 NOx PM10 SO2 NOx PM10 SO2 NOx PM10 SO2 NOx PM10 SO2 NOx PM10 SO2 NOx PM10

Urban Waste

incineration with heat

recovery

District Heating Plant

with waste and

biomass

High efficiency

domestic boilers

Energy efficiency in

building Low emission zones

Incentives for new

cars

SO2, NOx, PM10 emission saved (%) on total regional emission calculated respect to the CLE scenario at 2010

1 = Urban Waste incineration with heat recovery; 2 = Biogas recovery in agricultural and in farming sectors; 3 = District heating Plant with waste and biomass; 4 = Photovoltaic; 5 = Wind; 6 = Hydroelectric; 7 = Geothermic Well; 8 = High efficiency domestic boilers; 9 = Energy efficiency in building; 10 = Residential heating accountability; 11 = Heat pumps;

12 = Solar heating systems; 13 = Regulation of some fuel use; 14 = Incentives for shift to natural gas in domestic boilers; 15 = Efficiency improvements in fireplaces and stoves; 16 = Low emission zones; 17 = Road traffic restriction; 18 = Pollution charge; 19 = Car sharing; 20 = Motorway speed limits; 21 = Bike sharing; 22 = Incentives for new cars;

23 = Incentives for new diesel heavy duty; 24 = Opening new rail lines; 25 = Opening new underground lines; 26 = Cycle paths; 27 = Sea motorway; 28 = Bus investment (new buses, service extension, frequency increase); 29 = Antiparticulate filter; 30 = Incentives for biofuel public transport; 31 = New methane service stations; 32 = Incentive for hydrogen cars; 33 = Rationalising load transport in urban area;

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33Measures

no

. re

gio

ns

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

Contribution (%) by measure to SO2 emission reductionsContribution (%) by measure to NOx emission reductions

Contribution (%) by measure to PM10 emission reductions

Energy, Domestic and Transport measure adoption frequency in regions

CLE “CURRENT LEGISLATION” SCENARIO vs AQ PLANS SCENARIO IN 2010: PM10 CONCENTRATIONS

CLE “CURRENT LEGISLATION” SCENARIO vs AQ PLANS SCENARIO IN 2010: Life Expectancy Reduction

Documents

Integrated Assesments Models for Air Pollutions