Atmospheric pollution models

•Air Pollution Modelling•Description•Results

•Population Exposure Modelling

1. Workshop d. 6/2-7/2-2008

A. Gross, A. Baklanov, U. S. Korsholm, J. H. Sørensen, A. Mahura & A. Rasmussen

Contents:

Air pollution, transport and deposition

Emissionmodelling

Population

Meteorology / climateRef.-year: 2000

Health effects.

Externality cost functions

Energy system optimisation model(s)

Environmental impact and damage

Global externality cost for CO2

Scenarios for energy production

2010, 2020, 2030, 2040, 2050

Energy systems

Economic growth

Tech-nologies

EMEP, EDGAR, IPCC, etc.

CEEH modelling framework: Model Components

Thresholds of Air Pollutants

. Threshold Av. per. Effects on humans

O3

Population information Population warning

180 μg/m3 1 h./max.

240 μg/m3 1 h./max. 3 h.

Reduced lung function, chest pain, breathing problems, headache, eye irritations.

SO2

Population information

350 μg/m3 1 h./24 times

125 μg/m3 24 h.

350 μg/m3 1 h./max.

Reduced lung function, breathing problems, increased mortality.

NO2

Population information 350 μg/m3 1 h./max.

Reduced lung function and difficulty in breathing. Increased possibility of infections.

CO 10 mg/m3 8 hours Taken up in the blood, i.e. reduces the bloods transport of oxygen.

Particular Matter: PM10 50 μg/m3 24 hs/35 times

40 μg/m3 yearly

Respiratory tract and cardiovascular diseases.

Benzene 5 μg/m3 yearly Carcinogenic.

Pb 0.5 μg/m3 yearly/max. Damage the nerves.

Red from the Danish Smog and ozoneberedskabet Green from EU

Atmospheric Chemical Aerosol Transport

Emissions

NOx

Biogenic Compounds

Hydrocarbons

ChemistryNO2 + h (+ O2) O3 O3 + NO NO2 + O2

HO + NO2 HNO3

Meteorology

Cloud Processes

Deposition

Environment

Ozone

Aerosols

Air Quality Modeling Framework

Air Quality Monitoring Data•Chemical Initial Conditions•Lateral and Top Boundary Conditions

MeteorologicalObservations

Air Quality ForecastTime Resolved 3-Dimensional Fields of Air Pollutants and 2-Dimensional Deposition Patterns

Air Quality Model or

Subroutine

Meteorological Forecast ModelMeteorological ParametersFields of Winds, Temperatures, Humidity and others

Transport•3-D advection•vertical diffusion

Cloud Effects•Aqueous chemistry•wet scavenging•vertical redistribution

Modules in Air Quality Model

Dry Deposition•Loss to surfaces by non-precipitation processes

Gas-Phase Chemistry

•Integration of chemical rate equations for VOC, NOx, SO2 and ozone

Emissions•Determines emissions of VOC, NOx, SO2 and CO

LOG(NOx, ppb)

ppb

-2-1

01

23

-1

0

1

2

3

4

LOG(VOC, ppbC)

100

200

O3

-2

-10

12

3

LOG(NOx, ppb)

-1

01

23

4

LOG(VOC, ppbC)

2.5

5.0ppb

H2O2

HO

-1

0

1

2

3

4

LOG(VOC, ppbC)

-2

-1

0

1

2

3

LOG(NOx, ppb)

0.20.4

0.60.8

ppt ppb

HNO3

10

20

-2

-1

0

1

2

3

LOG(NOx, ppb)

-1

0

1

2

3

4

LOG(VOC, ppbC)

NOx and VOC Dependence

Three Air Pollution Model Types

Approaches:Normal distribution,Bin approach

Physics:• Condensation• Evaporation• Emission• Nucleation• Deposition• Coagulation

Aerosol Module

1. Gas Phase2. Aqueous phase3. Chemical equil.4. Climate Modeling

Chemical Solvers

ECMWF

DMI-HIRLAM

Eulerian trans-port 0.2-0.05lat-lon, 25-40 vert. layer, 3-D regional scale

StochasticLagrangian transport,3-D regional scale

On-Line Chemical Aerosol Trans.EnviroHIRLAM

Off-Line Chemical Aerosol Trans.

CAC

Emergency Pre-parednes & Risk Assess-

ment. DERMA

Nuclear, veterinary and chemical.

Regional (European) to city scale air pollution: smog and ozone.

Regional (European) to city scale air pollution: smog and ozone.

Tropo. Trans. Models

Met. Models

Off-Line modelling with CAC

T:0.15º×0.15º

S: 0.05º×0.05º

CAC Model Area

Currently nested versions of HIRLAM 60 vertical layers:• T – 15x15 km2.• S – 5x5 km2. • Q – 5x5 km2.• U – 1.5x1.5 km2 of DK.

A forecast integration starts out by assimilation of meteorological observations whereby a 3-d state of the atmosphere is produced, which as well as possible is in accordance with the observations.

• CAC 20x20 km2

24 hour forecast

48 hour forecast

0 15 30 60 90 120 150

ppbV

CAC forecast, December 20, 2007O3, NO, NO2, CO, SO2, Rn, Pb, PM2.5, PM10.

Ozone NO2 X 1000

CAC Air Quality Modelling

Ozone Modelling from August 8 to 12, 2003

Jægersborg

Ulborg

Keldsnor

Lille Valby

X: CAC forecastsX: observations

CloudCondensation

Nuclei

Precipitation

Chemistry/Aerosols

On-line air-quality modelling

Cloud-radiationInteraction

&Radiation budgets

Temperature profiles

Chemistry/Aerosols

I.e. models which includes feedbacks of chemistry and aerosol on NWP

At present only two meso-scale models with feedbacks exists (indirects effects of aerosols)

•WRFChem (developed by NCAR)•EnviroHIRLAM (developed by DMI)

Accumulated (reference) dry deposition [μg/m2] +48 h Difference (ref – perturbation) inAccumulated dry deposition [ng/m2]

More details – Ulrik Korsholms

presentation tomorrow

Accidental fire in waste deposit Aalborg Portland 23 October 2005

DERMA calculations

Population Exposure: Scheme of the suggested improvements of meteorological forecasts (NWP) in urban areas, interfaces to and integration with UAP and PE models

Urban Air Pollution models

Population Exposure models

Populations/

Groups Indoor concentrations

Outdoor concentrations

Time activity

Micro-

environments E x p o s u r e

Urban heat flux parametrisation

Soil and sublayer models for urban areas

Urban roughness classification &

parameterisation

Usage of satellite information on

surface

Meso- / City - scale NWP models

Mixing height and eddy diffusivity estimation

Down-scaled models or ABL

parameterisations

Estimation of additional advanced

meteorological parameters for UAP

Grid adaptation and interpolation,

assimilation of NWP data

WP5: Interface to Urban Air Pollution models

WP4: Meteorological models for urban areas

WP7:

FUMAPEX target cities for improved UAQIFS implementation

#1 – Oslo, Norway

#2 – Turin, Italy

#3 – Helsinki, Finland

#4 – Valencia/Castellon, Spain

#5 – Bologna, Italy

#6 – Copenhagen, Denmark

Different ways of the UAQIFS implementation:

(i) urban air quality forecasting mode,

(ii) urban management and planning mode,

(iii) public health assessment and exposure prediction mode,

(iv) urban emergency preparedness system.

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0 2 4 6 8 10 12 14 16 18 20 22 24

Time of day

Fra

ctio

n o

f ho

ur

Traffic

Home

Work

Other

Time activity of the population

Home location Workplaces

FUMAPEX: KTL

At home

Exposure PM2.5 (μg/m3×persons)

At other locations

Population Exposure: inversion day

FUMAPEX: KTL & FMI

04/22/23 Erik Bødtker, Danmarks Meteorologiske Institut

GIS: Doses Individual and Collective

Ignalina Nuclear Power Plant (Lithuania), Cs-137

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1. Workshop d. 6/2-7/2-2008

Thank you for your attention!