The experience of piedmont in Air Quality

Workshop on the preparation of air quality
[Ing. Giorgio Arduino, Reg. Piemonte, Italy]
Workshop on the preparation of air quality plansWorkshop on the preparation of air quality plans
organised in co-operation with theorganised in co-operation with the
Ministry of Environment andMinistry of Environment and
Spatial PlanningSpatial Planning
Experiences in establishment ofExperiences in establishment of
zones and agglomerationszones and agglomerations
the Case of Piedmont regionthe Case of Piedmont region
(Italy)(Italy)

Foreword Italian Regions are the Institutions committed
to perform air quality assessment required by
EU directives
Tasks:
•Subdivide territory in zones defined
depending on the risk level of exceeding air
quality limits
•Realise yearly air quality assessment
elaborate air quality information to be
transmitted to the European Commission
•Periodically revise zones definition
•Define mitigation actions and pollution
reduction plans
Experiences in establishment of zones and agglomerations: PiedmontExperiences in establishment of zones and agglomerations: Piedmont

Piedmont Region:
a complex territory
Mountains
43%
Foothill
30%
Lowlands
27%
 Area:Area: 25.42625.426 Km²
 Climate:Climate:
Winters: cold, dry and banks of fog
Summers: cool in the hills and quite hot in the plains
 Temperature range during the year:Temperature range during the year: - 5°C / +30°C- 5°C / +30°C
 HydrographyHydrography: more than 4000 km
 Land coverLand cover: 52%52% agricultural, 40,2%40,2% forest and semi natural, 6,3%6,3% artificial,
1,5%1,5% water bodies-wetlands
 Protected areasProtected areas: 22 national parks, 6767 regional parks, 8,3%8,3% of the total surface
 Nature 2000 networkNature 2000 network: 123123 SCI, 5151 SPA, 15,67%15,67% of the total surface
 Monitoring stationsMonitoring stations
Air: 85 Water: 129 Meteorological: 322
Experiences in establishment of zones and agglomerations:Experiences in establishment of zones and agglomerations:
PiedmontPiedmont

 Population:Population: 4.290.0004.290.000
 Density:Density: 169 pop/Km²169 pop/Km²
 88 ProvincesProvinces - 1.206- 1.206 MunicipalitiesMunicipalities
 Chief Town:Chief Town: Turin ( 901.000)Turin ( 901.000)
 AgglomerateAgglomerate: Turin + 11 ( 1.297.000): Turin + 11 ( 1.297.000)
 Road net:Road net: 22.630 Km22.630 Km ( first in Italy !!! )( first in Italy !!! )
 Vehicles:Vehicles: 3.481.7363.481.736
GDP 2006:GDP 2006: 119 Bil €119 Bil € (10% GDP Italy)(10% GDP Italy)
Businesses:Businesses: 407.137407.137
Agriculture
18%
Industries
28%
Services
54%
Piedmont Region: a complex territory

 Several subjects provide the source information: Region, Provinces,
Environmental Agency, Municipalities, Private organizations….
 Environmental information creation process is transversal to the
public bodies with specific competence
PROVINCES
Permitting procedures and
local planning
REGIONAL ENVIRONMENT
PROTECTION AGENCY (ARPA)
Technical control and monitoring
REGION
Planning and coordinating
Environmental
Information
System
Environmental information in Piedmont

Regional Air Quality
Network • 74 public fixed stations74 public fixed stations
• 6 public mobile stations6 public mobile stations
• 11 private fixed stations11 private fixed stations
In the fixed network there are:
65 nitrogen oxide analyzers (NOx)
47 carbon monoxide analyzers (CO)
54 PM10 samplers and analyzers
2 PM2.5 samplers
25 sulphur dioxide analyzers (SO2)
33 ozone analyzers (O3)
16 aromatic hydrocarbon analysers (BTX)

 Air Quality ManagementAir Quality Management
……air quality models have an important place in air qualityair quality models have an important place in air quality
management. They are essential tools in the development of actionmanagement. They are essential tools in the development of action
plans for improving air quality, which is the ultimate goal of theplans for improving air quality, which is the ultimate goal of the
Member States and local authorities in order to fulfil their obligationsMember States and local authorities in order to fulfil their obligations
under the directives.under the directives.
From: Guidance on Assessment under the EU Air Quality Directives, EEA 2002From: Guidance on Assessment under the EU Air Quality Directives, EEA 2002

 Zoning for Air Quality ManagementZoning for Air Quality Management
Starting from the preliminary assessmentStarting from the preliminary assessment
DIR 1996/62/CEDIR 1996/62/CE
(art. 5)(art. 5)
Member States which do not have representative measurements of theMember States which do not have representative measurements of the
levels of pollutants for all zones and agglomerations shall undertakelevels of pollutants for all zones and agglomerations shall undertake
series of representative measurements, surveys or assessments inseries of representative measurements, surveys or assessments in
order to have the data available in time for implementation of theorder to have the data available in time for implementation of the
legislation referred to in Article 4 (1).legislation referred to in Article 4 (1).

Following the EU Framework Directive (96/62/EC)
and his Daughter Directives (1999/30/EC; 2000/69/EC; 2002/3/EC; 2004/107/EC)
… and now New Air Quality Directive (2008/50/EC)
Zones and assessmentZones and assessment
regimesregimes
From: Guidance on Assessment under the EU Air Quality Directives, EEA 2002From: Guidance on Assessment under the EU Air Quality Directives, EEA 2002

 Zone, why ???Zone, why ???
 The new air quality directives oblige the Member States to divide theirThe new air quality directives oblige the Member States to divide their
territories in zonesterritories in zones
 Zones are primarily units for air quality managementZones are primarily units for air quality management
Municipal Administrative BoundariesMunicipal Administrative Boundaries
 but the directives also specify assessment requirements per Zonebut the directives also specify assessment requirements per Zone
Homogeneous Data on Air PollutionHomogeneous Data on Air Pollution
 These requirements depend on how far air quality levels are below a limitThese requirements depend on how far air quality levels are below a limit
valuevalue
Air Quality Data in All the TerritoryAir Quality Data in All the Territory

 it is clear that the collection of air quality data throughout the territory isit is clear that the collection of air quality data throughout the territory is
the priority in order to properly zoning the entire regionthe priority in order to properly zoning the entire region
 The Piedmont region in 1999 (year of implementation in Italy) did not haveThe Piedmont region in 1999 (year of implementation in Italy) did not have
an adequate network of continuous monitoringan adequate network of continuous monitoring
 The estimation methodology used in reference to the limits of long-termThe estimation methodology used in reference to the limits of long-term
(annual average) for nitrogen dioxide and PM10 is based on the correlation(annual average) for nitrogen dioxide and PM10 is based on the correlation
between the amount of pollutant emitted annually per unit area in a givenbetween the amount of pollutant emitted annually per unit area in a given
municipality, and the concentrations found in the same town stations ofmunicipality, and the concentrations found in the same town stations of
regional air quality monitoring networkregional air quality monitoring network
 The simplifying assumptions underlying this approach is to consider that theThe simplifying assumptions underlying this approach is to consider that the
average concentration of a pollutant in the territory of a municipality isaverage concentration of a pollutant in the territory of a municipality is
substantially dependent on sources of emissions within the samesubstantially dependent on sources of emissions within the same
municipalitymunicipality
 It should be stressed that the methodology based on regional emissionsIt should be stressed that the methodology based on regional emissions
inventory provides, at present, an estimate of the average concentration ofinventory provides, at present, an estimate of the average concentration of
a pollutant in the territory of a municipalitya pollutant in the territory of a municipality

 So, 10 years ago, we have used one incorrect methodology forSo, 10 years ago, we have used one incorrect methodology for
zoning an entire region, using only few monitoring data and emissionzoning an entire region, using only few monitoring data and emission
data (not so bad) for every municipalities !data (not so bad) for every municipalities !
This is the correlation between emission NOThis is the correlation between emission NOxx [t/km[t/km22
] and NO] and NO22 [[µµg/mg/m33
]]

 Regional Emission Inventory: yearly total emissionRegional Emission Inventory: yearly total emission
NOx NMVOC PM10

 PiemontePiemonte : Zones and agglomerations (after preliminary assessment): Zones and agglomerations (after preliminary assessment)

CTM application: air quality assessment & zoning
According to the European Directive 96/62/EC and the ItalianAccording to the European Directive 96/62/EC and the Italian
Legislation (D.L. 351/1999), the Regional administrations are in chargeLegislation (D.L. 351/1999), the Regional administrations are in charge
to perform theto perform the air quality assessment (AQA)air quality assessment (AQA) in order to divide theirin order to divide their
territories in homogeneous zones and assess air quality within them,territories in homogeneous zones and assess air quality within them,
taking into account the different pollutants’ limit and threshold valuestaking into account the different pollutants’ limit and threshold values
Since 2005, Piemonte authorities have charged the ARPA Piemonte toSince 2005, Piemonte authorities have charged the ARPA Piemonte to
perform yearly AQA integrating the information provided by theperform yearly AQA integrating the information provided by the
regional monitoring network with concentration fields supplied by aregional monitoring network with concentration fields supplied by a
CTM modelling system.CTM modelling system.
The modelling system has been built and optimised performing yearThe modelling system has been built and optimised performing year
2004 AQA and successively applied for year 2005, 2006 and 2007.2004 AQA and successively applied for year 2005, 2006 and 2007.
The modelling system works on a computational domain covering theThe modelling system works on a computational domain covering the
whole Piemonte Region with an horizontal resolution of 4 km and 12whole Piemonte Region with an horizontal resolution of 4 km and 12
vertical levels, spanning the lower 3500 metres of the atmosphere.vertical levels, spanning the lower 3500 metres of the atmosphere.

Air quality assessment modeling system architectureAir quality assessment modeling system architecture
Pollutants: SOPollutants: SO22
NONOxx
NONO22
CO PMCO PM1010
PMPM2.52.5
OO33
CC66
HH66
Simulation timeSimulation time: 1 year: 1 year
Temporal resolutionTemporal resolution: 1 hour: 1 hour
Meteorological fields reconstructed integrating analysed fields from the European Centre forMeteorological fields reconstructed integrating analysed fields from the European Centre for
Medium Range Weather Forecast (ECMWF) with observations (radiosoundings and surfaceMedium Range Weather Forecast (ECMWF) with observations (radiosoundings and surface
data provided by the regional meteorological network) by means of a mass-consistent modeldata provided by the regional meteorological network) by means of a mass-consistent model
Boundary conditions obtained assimilating continental runs of the CTM CHIMERE and air qualityBoundary conditions obtained assimilating continental runs of the CTM CHIMERE and air quality
monitoring observationsmonitoring observations
Post-processing tools to compute all the indicators required by EU air quality legislation.Post-processing tools to compute all the indicators required by EU air quality legislation.
220 x 284 km2
∆xy: 4 km
Nx = 56
Ny= 72
Vertical levels: 12 (up to 3500m agl)
Simulation length: 1 year
Output fields time resolution: 1hour

METEOROLOGICAL DATA
MINERVE
SURFPRO
EMISSION
MANAGER
FARM
GEOGRAPHICAL
DATA
EMISSION DATA
METEOROLOGICAL INPUT
EMISSION INPUT
IC/BC
Modelling system main modules

 FARM:FARM: (Flexible Air quality Regional Model)(Flexible Air quality Regional Model)
 3D Eulerian model, derived from STEM-II3D Eulerian model, derived from STEM-II (Carmichael et al.)(Carmichael et al.)
 Diffuse sources & LPS with plume riseDiffuse sources & LPS with plume rise
 Horizontal adv.-diff.: Blackman cubic polynomialsHorizontal adv.-diff.: Blackman cubic polynomials (Yamartino, 1993)(Yamartino, 1993)
 Vertical adv.-diff.: hybrid semi-implicit Crank-Nicolson / fully implicitVertical adv.-diff.: hybrid semi-implicit Crank-Nicolson / fully implicit
schemescheme (Yamartino et al., 1992)(Yamartino et al., 1992)
 Actinic flux reduction effect from cloudsActinic flux reduction effect from clouds
 SOx-NOx-NH3 simplified schemeSOx-NOx-NH3 simplified scheme (EMEP)(EMEP)
 Photochemistry: SAPRC-90 chemical schemePhotochemistry: SAPRC-90 chemical scheme
 PM: Models-3/CMAQ aero-3 modulePM: Models-3/CMAQ aero-3 module (Binkowski, 1999)(Binkowski, 1999); aero-0 simplified; aero-0 simplified
bulk modulebulk module
 One- or two-way nestingOne- or two-way nesting

Emission dataEmission data The Emission component is prepared from IREA (Regional Emission Inventory) by EMMA code (Arianet),
to obtain hourly emission rates gridded on the regional domain and nearby regions. The emissions inside IREA
are defined on a polygonal bases (details on municipal or provincial boundaries) and a specific module of EMMA
is able to take into account the characteristics of land-use components inside each cell, so that emissions
associated with individual human activities (production of energy, agriculture, farming, transport, waste
treatment, etc ...) or natural (biogenic, soil dust, ecc. ) are distributed only into the cells of the domain intersected
by each polygon emission and the appropriate land-use type.
DTM 250 m
CORINE LC
250 m
Regional
transport
graph
Urban area from
CTR
INEMAR
Piemonte
EMEP
CORINAIR
Italy
Valle d'Aosta
Regional
Inventory
INEMAR
Lombardia

Emission data treatmentEmission data treatment
Example:Example:
A: distribution of conifers by CORINE Land CoverA: distribution of conifers by CORINE Land Cover
B: spatialisation biogenic emissions from conifer grills on the domainB: spatialisation biogenic emissions from conifer grills on the domain
A
B

99 total observations
Observations:
Wind, T, Q
30 grid points (0.5° lat lon
resolution), every 6 hours
ECMWF Analyses:
(Cuneo Levaldigi e Milano Linate)
2 Radiosoundings: VV, DV, T, RH
VV, DV, T, P, RH
(hourly data)
94 surface stations:
3 WMO (Synop): VV, DV, T, P, RH,
(every 3 hours)
 Meteorological data

 Stations & fields used to define initial and boundary conditionsStations & fields used to define initial and boundary conditions
Chimere Continental run provided by Prev’Air
• 0.5 ° space resolution, 8 vertical levels
• Chemical mechanism: MELCHIOR + aerosol
Air Quality measurements from Piemonte and
nearby Italian Regions networks

 Evaluation of model performances: PMEvaluation of model performances: PM1010
2005 – PM10 yearly averages 2005 – PM10 daily averages
Station: Torino-Consolata

 Evaluation of model performances: NOEvaluation of model performances: NO22
2005 – NO2 daily averages
Station: Torino-Rebaudengo (roadside)
2005 – NO2 hourly averages

 Evaluation of model performances: OEvaluation of model performances: O33
2005 – O3 max 8-hour average 2005 – O3 max 8-hour average
Station: Novara-Verdi (urban – background)Station: Buttigliera d’Asti (rural – background)

 The Piemonte Region Air Quality Assessment modelling system has been evaluated comparingThe Piemonte Region Air Quality Assessment modelling system has been evaluated comparing
modelled concentrations with measurements from stations located in different geographic, topographicmodelled concentrations with measurements from stations located in different geographic, topographic
and land cover environment.and land cover environment.
 O3 indicators shows satisfactory results, with good accuracy in reproducing measured levels, for bothO3 indicators shows satisfactory results, with good accuracy in reproducing measured levels, for both
eight-hours and hourly averages.eight-hours and hourly averages.
 NO2 concentration fields are satisfactorily reproduced, yearly averages and concentration statisticsNO2 concentration fields are satisfactorily reproduced, yearly averages and concentration statistics
are compatible with measured values. The model accuracy remains within the requested limit for mostare compatible with measured values. The model accuracy remains within the requested limit for most
monitoring sites. Some underestimation of peak concentration has been detected where modelmonitoring sites. Some underestimation of peak concentration has been detected where model
resolution is insufficient and during adverse meteorological conditions (severe episodes).resolution is insufficient and during adverse meteorological conditions (severe episodes).
 PM10 concentrations have been reconstructed with values within the requested accuracy for bothPM10 concentrations have been reconstructed with values within the requested accuracy for both
daily and yearly averages at most station locations, even though a clear underestimation can bedaily and yearly averages at most station locations, even though a clear underestimation can be
noticed for stations located outside the Torino metropolitan area.noticed for stations located outside the Torino metropolitan area.
This shortcoming can be partly ascribed to a certain underestimation of emissions outside Torino (e.g.This shortcoming can be partly ascribed to a certain underestimation of emissions outside Torino (e.g.
from wood burning) and to the difficulty to simulate PM accumulation phenomena within the Po valley.from wood burning) and to the difficulty to simulate PM accumulation phenomena within the Po valley.
 Regione Piemonte implemented the modelling system as a permanent air quality assessment tool andRegione Piemonte implemented the modelling system as a permanent air quality assessment tool and
ARPA Piemonte is now in charge to perform modelling applications on a yearly basis.ARPA Piemonte is now in charge to perform modelling applications on a yearly basis.

 Piemonte 2005: PMPiemonte 2005: PM1010 - Zones and agglomerations- Zones and agglomerations

 Piemonte 2005: NOPiemonte 2005: NO22 - Zones and agglomerations- Zones and agglomerations

 Piemonte 2005: OPiemonte 2005: O33 - Zones and agglomerations- Zones and agglomerations

 …… the problem is:the problem is:
PM10 2005
yearly average
The AQA modelling system
provides data on a regular grid
We need to aggregate data at municipality
scale (“change of support problem”)

 The output fields produced by the modeling system are superimposed (usingThe output fields produced by the modeling system are superimposed (using
GIS GRASS) to vector thematic maps that describe the region:GIS GRASS) to vector thematic maps that describe the region:
 in this way are identified for each administrative unit, the cells that fall within it,in this way are identified for each administrative unit, the cells that fall within it,
and, on the basis of intersection polygon-cells, the breakdown of communaland, on the basis of intersection polygon-cells, the breakdown of communal
cellscells
Note: the grid points belonging to the municipality of Alessandria
However, very few municipalities inHowever, very few municipalities in
Piedmont have more than 5 dataPiedmont have more than 5 data

 the aggregation at municipality scale can be realized solving the so-calledthe aggregation at municipality scale can be realized solving the so-called
“change of support problem”.“change of support problem”.
 A simple solution is to integrate over the area, that means to average theA simple solution is to integrate over the area, that means to average the
field values weighted by areas over the cells belonging to a certainfield values weighted by areas over the cells belonging to a certain
municipality.municipality.
 Two alternatives for the aggregation are the average of the field valuesTwo alternatives for the aggregation are the average of the field values
weightedweighted by theby the building percentagebuilding percentage for every cell (a point in the gridfor every cell (a point in the grid
represent a cell) and therepresent a cell) and the 90th percentile90th percentile over the cell values in aover the cell values in a
municipality.municipality.
 TheThe building percentagebuilding percentage is an important indicator of theis an important indicator of the antropicantropic
activityactivity which could generate more pollution in a municipality, whereas awhich could generate more pollution in a municipality, whereas a
wide country area could not contribute at all.wide country area could not contribute at all.
 Instead theInstead the 90th percentile90th percentile is chosen as a measure ofis chosen as a measure of extreme casesextreme cases
in a municipality, in a precautionary perspective.in a municipality, in a precautionary perspective.

 MethodologyMethodology
(1) Weighted block average (weight = municipality area percentage)
〈C i〉=∑
j=1
npi
ijc j
ij=
Aij
Ai
(2) Weighted block average (weight = municipality built area percentage)
〈C i〉=∑
j=1
npi

ijc j e

ij=
Eij
Ei
(3) Weighted block average (weight obtained using municipality area and
build area percentage)
〈C i〉=1〈C1i〉2 〈C2i〉 con 〈C1i〉=∑
j=1
npi
Iij1ijc j e 1ij=
Aij
Ai∣Iij=1
dove Iij=1 se
Eij
Aij
≥ , Iij=0 se
Eij
Aij

e con 〈C2i 〉=∑
j=1
npi
1− Iij2ijc j e 2ij=
Aij
Ai∣1−Iij=1
(4) 90th percentile
or maximum over the cell valuea in a municipality

 Comparison between the three algorithms (examples of Biella, whereComparison between the three algorithms (examples of Biella, where
we see major differences - with built concentrated in a restricted area)we see major differences - with built concentrated in a restricted area)
and Torino (little difference)and Torino (little difference)

 Functional approach for land classification using data coming from AQAFunctional approach for land classification using data coming from AQA
modelling system - Cluster Analysismodelling system - Cluster Analysis
 Pollutant time series can be considered as realizations of continuousPollutant time series can be considered as realizations of continuous
processes that are recorded in discrete time. The conversion from discreteprocesses that are recorded in discrete time. The conversion from discrete
data to curves involves smoothing, using linear combinations of B-splinedata to curves involves smoothing, using linear combinations of B-spline
functions. Thus we have functional data that can be classified by clusteringfunctions. Thus we have functional data that can be classified by clustering
procedures. The non hierarchical algorithm PAM (Partioning Around Medoid)procedures. The non hierarchical algorithm PAM (Partioning Around Medoid)
is used to cluster objects. The parameterization of functions using B-splinesis used to cluster objects. The parameterization of functions using B-splines
can replace the classical analysis by synthetic indicators (as average levels,can replace the classical analysis by synthetic indicators (as average levels,
standard deviations, quantiles) to respect the functional form of pollutantstandard deviations, quantiles) to respect the functional form of pollutant
concentrations through time. Synthesizing features contained within timeconcentrations through time. Synthesizing features contained within time
series in a little number of coefficients, we preserve in the “zoning” mostseries in a little number of coefficients, we preserve in the “zoning” most
information about pollutant concentration levels and time profiles.information about pollutant concentration levels and time profiles.
 This approach is applied to functional data of the biggest criticalThis approach is applied to functional data of the biggest critical
atmospheric pollutants in Piemonte.atmospheric pollutants in Piemonte.

 Clustering resultsClustering results
NO2 (left, 4 cluster) and PM10 (right, 3 cluster)
Data coming from simulations for AQA 2005

 Multi-pollutant zoningMulti-pollutant zoning
To support decisors considering jointly differentTo support decisors considering jointly different
pollutants, in order to get a multi-pollutantpollutants, in order to get a multi-pollutant
zoning, we propose to summarize them in anzoning, we propose to summarize them in an
air quality index (1) or, alternatively, in principalair quality index (1) or, alternatively, in principal
components (2).components (2).
(1)(1) We propose to aggregate over pollutants by theWe propose to aggregate over pollutants by the
maximum function, in order to keep informationmaximum function, in order to keep information
about critical cases, to obtain air quality indexabout critical cases, to obtain air quality index
time series for all the municipalities.time series for all the municipalities.
Considering these time series as functionalConsidering these time series as functional
data we can cluster them and obtain groups ofdata we can cluster them and obtain groups of
municipalities, through a functional clustermunicipalities, through a functional cluster
analysis where PAM algorithm is embeddedanalysis where PAM algorithm is embedded

 Multi-pollutant zoningMulti-pollutant zoning
(1)(1) As alternative technique, we explore theAs alternative technique, we explore the
Functional Principal Component AnalysisFunctional Principal Component Analysis
(FPCA) in its multivariate version, since we(FPCA) in its multivariate version, since we
consider different pollutants taking intoconsider different pollutants taking into
account their interactions. Then we applyaccount their interactions. Then we apply
the PAM algorithm to the scores of thethe PAM algorithm to the scores of the
principal components, obtaining groups ofprincipal components, obtaining groups of
municipalities. In this case the medoids aremunicipalities. In this case the medoids are
scores where the temporal component isscores where the temporal component is
integrated outintegrated out

 The DifferenceThe Difference
1999 2009

 Air Quality PlanAir Quality Plan
……only using quality models we can take good plan with reasonableonly using quality models we can take good plan with reasonable
scenario.scenario.
Italy:Italy: national, regional, provincial & municipal plansnational, regional, provincial & municipal plans
 Regional decision maker: … LV will be respected ?
… role of local measures ?
 Develop coherent evaluations, taking into account:
 local features (sources, topography & land-use, meteorology…)
 broader context

Regional AQ system
Boundary
conditions
Meteo modelEmission
manager
Dispersion
& chemistry
Impacts
Reference
Inventory
Regional plan
Emission
scenario
Atmospheric simulation
system
Costs Emissions
Atmospheric dispersion
Health & env. impacts
Enargy / ind. / agric.
Projections
Emission control
RAINS-Italy
RAIL

 MethodologyMethodology
1.1. Inventories harmonization:Inventories harmonization: IREA (Regional Emission Inventory)IREA (Regional Emission Inventory)
vs. RAINS-Italy, for baseline yearvs. RAINS-Italy, for baseline year
2.2. Projected emission scenario:Projected emission scenario: RAINS-Italy CLE, downscaled atRAINS-Italy CLE, downscaled at
municipality level using IREAmunicipality level using IREA
3.3. Regional Plan emission scenarios:Regional Plan emission scenarios: translation (at municipalitytranslation (at municipality
level) of local measureslevel) of local measures
4.4. Impacts on concentrations:Impacts on concentrations: simulation through regional 3Dsimulation through regional 3D
atmospheric modelling systematmospheric modelling system
Ongoing process, promoted by the Ministry of the Environment, with the progressive involvement of Regional GovernmentsOngoing process, promoted by the Ministry of the Environment, with the progressive involvement of Regional Governments

 Piemonte AQ Plan: measures at 2010
 Heating
• energy efficiency (new & renovated)
• boilers: efficiency & emission limits
• ban of dirtier fuels (coal and distillate oil)
• incentives for solar heating for sanitary water
• district heating expansion
 Transport
• whole region: progressive ban of the most polluting vehicles
• “Plan Zones”: restricted traffic zones in municipalities > 10000 inh.
• adoption of DPF (diesel particles filters)

 Piemonte Case: Effects on emissions

 Variations for “Plan 2010” scenarios respect to “Base 2005”
(all categories)

Detail on individual measures – Heating
“Plan 2010” vs. “Reference 2010” variations, by measure (local measure)

Detail on individual measures – Mobility
“Plan 2010” vs. “Reference 2010” variations, by type (example)

 Piemonte Case: Effects on concentration

 NO2 concentrations, yearly averages
 “Plan-R 2010” vs. “Baseline 2005” (CLE + local measures)

 PM10 concentrations, yearly averages
 “Plan-R 2010” vs. “Baseline 2005” (CLE + local measures)

 Room for improvement …?
(RAINS – technical measures)(RAINS – technical measures)

 Lessons learnedLessons learned
…… implementing integrated N policies: difficulties & suggestionsimplementing integrated N policies: difficulties & suggestions
There is room for local, coordinated policiesThere is room for local, coordinated policies
…… supported by regional- and urban-scale analysessupported by regional- and urban-scale analyses (spatial details, hotspots)(spatial details, hotspots)
…… linked to broader contextlinked to broader context
…… multiple models: need of consistent tools & data - harmonizationmultiple models: need of consistent tools & data - harmonization
Long-term perspectiveLong-term perspective (4/5 years…)(4/5 years…)
Lack of multidisciplinary connectionsLack of multidisciplinary connections (e.g. mobility, agriculture)(e.g. mobility, agriculture)
especially on “quantitative approaches”especially on “quantitative approaches”
…… Italian peculiarity ?Italian peculiarity ?

Thank YouThank You
ForFor
Your AttentionYour Attention

The experience of piedmont in Air Quality

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