Integrated Assesments Models for Air Pollutions
Transcript of 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
Economic activities Projections…
Driving forces 3
Emission control options
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
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
Economic activities Projections…
Driving forces 4
Emission control options
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
Scenario Emissioni NOX - ITALIA
NO2 Workshop, Aprile 2010 Bruxelles
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
Economic activities Projections…
Driving forces 4
Emission control options
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
Absolute error Relative error
Differences ATM-model
Control run “GAINS no CP 2020” Model run vs ATMs method
-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
Absolute error Relative error
Differences ATM-model
Control run “GAINS no CP 2020” Model run vs ATMs method
NO2
Absolute error Relative error
Differences ATM-model
-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
Control run “GAINS no CP 2020” Model run vs ATMs method
-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
Absolute error Relative error
Differences ATM-model
Control run “GAINS no CP 2020” Model run vs ATMs method
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
Absolute error Relative error
Differences ATM-model
Control run “minus 25” Model run vs ATMs method
-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
Absolute error Relative error
Differences ATM-model
Control run “minus 25” Model run vs ATMs method
NO2
Absolute error Relative error
Differences ATM-model
-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
Control run “minus 25” Model run vs ATMs method
-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
Absolute error Relative error
Differences ATM-model
Control run “minus 25” Model run vs ATMs method
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
OZONE AND 2ND ORDER COEFFICIENTS
-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
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?
THE “AVERAGE MATRIX”
HOW DO WE COMPUTE IT?
THE “AVERAGE MATRIX”
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