Modelling the spatial distribution of particulate matter in Switzerland

Federal Department of the Environment,Transport, Energy and Communications DETEC

Federal Office for the Environment FOEN

Modelling the spatial distribution of particulate matter in Switzerland

26. October 2006

Air Pollution Control and NIR Division

Rudolf Weber, Air Quality Management SectionSwiss Federal Office for the Environment

2 11th EIONET Workshop AQ Rudolf Weber

Is particulate matter a problem in Switzerland?

Switzerland1.3.1998

WHO AQ guidelines 2005

EU (1999/30/EC)

Annual mean

20 μg/m3 20 μg/m3 40 μg/m3

#days > 50 μg/m3

1 3 35

Air quality limit values


Measurements

More than 60 stations


Measured PM10-levels in Switzerland

2000 2001 2002 2003 2004 2005

Annual mean > 20 μg/m3 (CH)

60% 61% 74% 93% 70% 71%

Annual mean > 40 μg/m3 (EU)

0% 0% 0% 1% 1% 1%

Maximal annual mean

35 32 39 48 47 46

#24h-means >50μg/m3 CH EU

90%9%

97%5%

95%8%

100%20%

90%6%

78%14%

Maximal daily mean

165 142 174 150 213 178


PM10: EvolutionPM10 - annual means in Switzerland

(prior to 1997: PM10 calculated from TSP)

0

10

20

30

40

50

60

1991 1993 1995 1997 1999 2001 2003 2005

PM

10 [

µg

/m³]

City street

City background

Suburban

Rural

Foothills

from the National Air Pollution Monitoring Network NABEL

=> Constant since 2000


Maps

Why maps?

• Better visualization

• Population exposure

• Models based on emission grids allow reduction scenarios

Models

• 3d chemical models

• simple dispersion, no chemistry

• interpolation from measured data


Modelling concept

BUWAL report: UM-169, 2003 „Modelling of PM10 and PM2.5 ambient concentrations in Switzerland“

Source-receptor matrix; 1-year meteorology

Emission grids with temporal cycles

Primary <-> secondary (from precursor maps)

Only annual mean values


Emissions

Emissions of primary particles• Road: traffic model „bottom-up“

• Rail: „top-down“

• Air: up to 200 m, ZH, GE

• Industry, households, agriculture/forestry: „top-down“, area statistics

Secondary particles

1) Concentration maps of precursors, spatially smoothed (reaction time)

• NO2 => NH4NO3

• SO2 => (NH4)2SO4

2) Emission grids of precursors

• anthropogenic und biogenic VOC


Background

Background (Imported and not modelled fractions)

from particulate sulfate: Height profile of primary PM

+ secondary PM (modell versus resulats of NFP41)

+ Contribution in Ticino

+ Contribution in Sottoceneri

Includes undidentified / not quantified Swiss sources

(like ships, air traffic > 200 m …)


Dispersion

Dispersion

Source-receptor method (Gaussian model)

• Near-area: 200 m-grid on 6 x 6 km2 area

• Far-area: 2 km-grid on 200 x 200 km2 area

Following TA Luft 1986, Stability from Swiss Meteorological Institute

Meteo-data: 1h-values of the year 1998

Source categories

• Elevated (20 m): Diurnal cycle in 4 seasons

• ground-level: Diurnal cycle

=> Different source-receptor matrices


Transfer functions for PM2.5

Short-range

1.E-03

1.E-02

1.E-01

1.E+00

[µg/m³]

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

IsotropicAlpine valley

Long-range

6 km 420 km


Climatological regions•Swiss Plateau region•Alpine valleys•remaining part of Switzerland


Summary transfer functions

short-range (6.2 km x 6.2 km: 31 x 31 cells, cell width 200 m)

1 emission height 2 m PM2.5 Plateau meteorology2 Symmetric meteorology3 Alpine meteorology4 PM10-PM2.5 Plateau meteorology5 Symmetric meteorology6 Alpine meteorology

7 emission height 20 m PM2.5 Plateau meteorology8 Symmetric meteorology9 Alpine meteorology

10 PM10-PM2.5 Plateau meteorology11 Symmetric meteorology12 Alpine meteorology

long-range (402 km x 402 km: 201 x 201 cells, cell width 2 km)

13 emission height 10 m PM2.5 Plateau meteorology14 Symmetric meteorology15 PM10-PM2.5 Plateau meteorology16 Symmetric meteorology


Flow chart of dispersion model

emissions road passenger transport, total PM10

PM2.5/PM10 ratio: 39%

39% 61%kELVP, kERVP gELVP, gERVP

emissions road passenger transport, PM2.5 emissions road passenger transport, PM10-PM2.5

MkLB MkRB MgLB MgRB

transfer function 1, 2 or 3 * transfer function 13 or 14 * transfer function 4, 5 or 6 * transfer function 15 or 16 *

kILVP kIRVP gILVP gIRVP

PM2.5 conc. due toroad passenger transport

(short-range)

PM2.5 conc. due toroad passenger transport

(long-range)

PM10-PM2.5 conc. due toroad passenger transport

(short-range)

PM10-PM2.5 conc. due toroad passenger transport

(long-range)

kIVP gIVP

PM2.5 conc. due to road passenger transport PM10-PM2.5 conc. due to road passenger transport

Idea: split emissions, sum up ambient concentrations

Example: road passenger traffic


Model structure

For secondary PM: conversion from precursor maps

primary PM inventories secondary PM inventories background(each for PM2.5 and PM10-PM2.5) (each for PM2.5 and PM10-PM2.5) concentration

road passenger transport secondary PM from anthropogenic VOC

road freight transport secondary PM from biogenic VOC

air transport nitrate from nitrogen oxides

rail transport sulphate from sulphur dioxide

industry/commerce ammonium from nitrate/sulfate neutral.

heavy equipment

residential

agriculture

forestry

PM2.5 concentration map

PM10-PM2.5 concentration map


Model versus measurements

0

10

20

30

40

50

0 10 20 30 40 50

PM10 measurements 2000 [µg/m³]

tota

l m

od

ell

ed

PM

10

[µ

g/m

³]

2000 model results

100:75

75:100

X = other

O = Ticino

+ = street canyon

– = alpine

1.33

0.75

Street canyon, flat model

Alpine valleys

Mod

elle

d P

M10

Measured PM10


Map of PM0


Map of PM2.5


Scenario “maximum feasible reduction”Primary PM: - 40%Precurors of secondary PM: - 20%

PM10 in 2010


maximum feasible reduction: PM2.5

PM2.5 in 2010


Population exposure

Average population eposure

Anthropogenic CH 9.5 μg/m3

Anthr. not-CH 8.7 μg/m3 (or ignored, underest., unknown)

Natural 1.4 μg/m3

Total 19.6 μg/m3

Influence of road traffic 22%

41% of population live in areas above annual air quality limit (annual mean > 20 μg/m3)

Measurements: road traffic 33% urban background, 45% street canyon


Population exposure from measurements

Idea: use the measured data of stations not as area representative but as type representative.

B

B

C

DHL

LM

P

R

ST

Z

J

0 10 20 30 40 50 60

01

02

03

04

0

NO2 (ug/m3)

PM

10

(u

g/m

3)

Stadtzentrum, an StrasseStadtzentrum, in ParkLändlich, an AutobahnAgglomerationLändlich < 1000 m ü.M.Ländlich > 1000 m ü.M.Hochgebirge

Types of stations in national network

Suburban


Average population exposure from measured data

0

5

10

15

20

25

30

35

40

1990 1995 2000 2005

An

nu

al m

ea

n P

M1

0 (

µg

/m³)

NABEL measurements

Dispersion model


Summary

Simple dispersion model agrees well with

measurements.

Estimation of population exposure with

station data is possible.

Use type of station, not simply the

location and area.

Future: Include soot (EC), BaP

Full chemical model (CamX)

Interpolation from measurements

Modelling the spatial distribution of particulate matter in Switzerland

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