1. Iakovos Barmpadimos, André Prévôt, Johannes Keller, Christoph Hüglin
Trends and meteorological influences on PM10, PM2.5 and PMcoarse in Switzerland and in Europe
Iakovos Barmpadimos, André Prévôt, Johannes Keller, Christoph Hüglin
Thanks to the Federal Office for the Environment and COST

2. Publications by Iakovos Barmpadimos
Barmpadimos, I., J. Keller, D. Oderbolz, C. Hueglin, and A. S. H. Prévôt (2012) One decade of parallel fine (PM2.5) and coarse (PM10–PM2.5) particulate matter measurements in Europe: trends and variability, Atmos. Chem. Phys., 12,
Barmpadimos, I., M. Nufer, D. C. Oderbolz, J. Keller, S. Aksoyoglu, C. Hueglin, U. Baltensperger, and A. S. H. Prevot (2011) The weekly cycle of ambient concentrations and traffic emissions of coarse (PM(10)-PM(2.5)) atmospheric particles, Atmospheric Environment, 45(27),
Barmpadimos, I., C. Hueglin, J. Keller, S. Henne, and A. S. H. Prevot (2011) Influence of meteorology on PM10 trends and variability in Switzerland from 1991 to 2008, Atmos. Chem. Phys., 11,

3. Aims
Investigate trends and variability of PM10, PM2.5 and PMcoarse in Switzerland and in Europe for the last two decades
Find out to what extend these trends are a result of meteorological variables and examine trends adjusted for meteorology
See which features of meteorological variables have the largest effect on PM concentrations

4. Data ~10 years of daily parallel PM10, PM2.5 measurements
5 rural background EMEP sites (blue)
1 suburban background (red)
1 urban background (orange)
~10 years of daily parallel PM10, PM2.5 measurements
EMEP = European Monitoring and Evaluation Programme

5. Methodology
Statistical regression of PM concentrations versus meteorological variables
Generalised Additive Models (GAM)
‘smooth’ function s

6. Methodology
Such smooth functions were fitted for many meteorological variables.

7. Methodology Example: Langenbruegge/Waldhof, summer
Process repeated for each size (PM10 and PM2.5), each season and for the whole-year data: Total 70 GAM

8. Methodology
“Adjusted” PM10

9. GAM performance and important variables
Between 50 and 65% of the observed variance was explained by GAMs
Variables selected for the 7 PM2.5 GAM runs for the whole-year datasets:
convective boundary layer depth
wind direction
wind speed
season
temperature
Julian day
precipitation
previous-day precipitation

10. Meteorological influence on PM2.5

11. Meteorological influence on PMcoarse

12. Influence of wind direction on PM2.5

13. Raw and adjusted trends
solid line: adjusted
dash line: raw
We get trends with the same confidence with less data
Average trend -0.4 μgm-3yr-1 for PM10 and PM2.5
No significant trend for PMcoarse
Decrease in PM10 is mostly the result of a decrease in the PM2.5 component

14. Data from Switzerland 13 stations of the NABEL network urban traffic
urban background
suburban
rural highway
rural lowland
rural mountain
(Hüglin et al., 2007)

15. PM10 trends in Switzerland
Average trend in the 90s: -1.0 μgm-3yr-1
Average trend in the 00s: -0.6 μgm-3yr-1
Trends differ between station types
Rural μgm-3yr-1
Urban background μgm-3yr-1
Urban street -1.2 μgm-3yr-1
blue: raw
red: adjusted

16. The weekly cycle of PMcoarse ambient concentrations

17. Traffic contribution to ambient PMcoarse
max conc.
min conc.
70%
53%
background
Sunday traffic contribution (c) is 53% with 95% confidence interval 34% - 78%
Weekday traffic contribution (d) is 70% with 95% confidence interval 57% - 86%

18. Conclusions (1)
PM trends after adjustment for the effect of meteorological factors have greater level of confidence
PM10 and PM2.5 decrease at several European regions (average -0.4 μgm-3yr-1)
PMcoarse have no significant trend
PM10 decrease at all Swiss sites (average -0.5 μgm-3yr-1), largely because of traffic
The rate of decline has slowed down in the 00s compared to the 90s

19. Conclusions (2) PMcoarse have a pronounced weekly cycle
Sunday PMcoarse traffic contribution is an estimated 53%
Weekday PMcoarse traffic contribution is an estimated 70%

20. Important
Effects of emissions on airquality trends can be detected earlier if meteorolgical adjustments are performed
There should be more data on Airbase or somewhere else including both airquality and! meteorological variables.