1. Concentrations of particulate matter in France : results and key findings
Olivier Le Bihan, François Mathe, Jean-Luc Houdret, Bertrand Bessagnet, Patrice Coddeville, Paolo Laj, Nathalie Poisson, Souad Bouallala, Cécile Honoré, Laure Malherbe, Laurence Rouïl

2. PM measurement network
- Implemented by 38 local organisations in charge of the AQ monitoring networks
- More than 300 PM10 monitoring stations (85% TEOM, 15% b gauges)
- 65 PM2.5 urban/suburban monitoring stations (TEOM)
- Field campaigns
- Specific sites devoted to scientific research (Puy-de-Dôme)

3. The puy de Dôme station Aerosol Parameters Continuous measurements
Size distribution (3-400 nm) - DMPS
Size distribution (125 nm- 10 µm) - OPC
Cluster ions (0,3 – 40 nm) - AIS
Inorganic and organic composition (OC-EC)
Organic speciation (CARBOSOL)
Scattering coefficient -Nephelometer 3l
Absorption coefficent -Aethalometer
Radionucleides Natural and artificial
Aerosol Mass
Aerosol Optical depth (2007)
Vertical distribution (LIDAR – 2007)
Precipitation and wet chemistry
Continuous measurements
Rainfall rate – Rain jauge
Droplet distribution – Doppler radar
Rain Inorganic and organic chemistry
Cloud physics (radius, LWC)
Cloud chemistry
Radionucleides Natural and artificial

4. Chemical composition dependent from the site typology and the season
PM10 analysis
- annual average varies from 22 to 25 ug/m3
- Highest concentrations obtained in Marseilles and Paris areas and at industrial sites
Chemical composition dependent from the site typology and the season

5. Special case of the Puy de Dôme station
Winter : 2 ug/m3
Summer : 5 ug/m3
A very large fraction of the organic component originates from biomass burning, especially during winter time. Increase in summer due to VOC oxydation (secondary OA)
( NO3 and NH4 measurements not considered )

6. Correlation between PM10 concentrations
- Based on daily PM10 averages
- Regressions between pairs of stations
- Slopes of the regression allow to identify linkages on the spatial pattern
- Correlation coefficients relies to the temporal aspect
- Seasonal analysis
Correlated geographical areas - summer
Correlated geographical areas - winter

7. PM 2.5 statistics Averages of daily concentrations
Highest urban concentrations in the south-West (15 ug/m3), in the south-East (17 ug/m3), in the Lyon area (15ug/m3): different reasons should be invoked
Lowest concentrations in the Western part of France (10 to 13 ug/m3)
Specific situation of the traffic sites in Paris area

8. Statistics related to “twins sites” in the North
Moy1 Moy2 Nbias(%) Var ratio Corr
(sub.) (urb.) Nbias =
Var ratio = ratio of variances
(suburban over urban)
Corr = correlation coefficient Summer
Different “increments” for different urban sites
Large sensitivity to the season
- Stronger correlation in summer
Winter Larger differences in variance in
winter

9. PM2.5 vs PM10 ratio (non volatile data)
Summers
Years
Winters

10. The volatile fraction issue
“Metrological” correction based on the implementation of FDMS devices for TEOM and RST devices for b gauges

11. Example : Peyrusse-Vieille (EMEP)
Relative combined uncertainty (DoE guidance) at the mean value : 13,6 % (2 µg/m3)
TEOM-FDMS/HVS
7th September – 15 th November 2005
Aver Conc : 15 µg/m3
Min : 5 µg/m3 Max : 34 µg/m3
Average Temp 15°C (Min 7 – Max 20°C)
Average Hum 80% (Min 57- Max 96%)
TEOM (50°C)/HVS
MATERIALS
2 HVS (gravim.) : U.B.S. = 0,64 µg/m3
2 TEOM-FDMS : U.B.S. = 0,86 µg/m3
1 TEOM (50°C)

12. PM Modelling as a complementary tool
- PM forecasts are provided by the PREV’AIR system (
- Based on CHIMERE model simulations
- Analysis of phenomenology especially long range pollution events (ammonium nitrate)

13. Prev’air scores (for PM10)
Lag of the forecast Rural stations Suburban stations
Observed mean (µg/m3) D (Nbr Obs: 358) 17.3 (Nbr Obs:1345)
D (Nbr Obs: 358) 17.3 (Nbr Obs:1346)
D (Nbr Obs: 351) 17.4 (Nbr Obs:1320)
D (Nbr Obs: 346) 17.5 (Nbr Obs:1298)
Simulated mean (µg/m3) D D D D
Normalized Bias (%) D D D D
NMSE (%) D D D D
Correlation D D D D
E50% (%) D D D D