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

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, 3189-3203. 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), 4580-4590. 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, 1813-1835.

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

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

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

Methodology Such smooth functions were fitted for many meteorological variables.

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

Methodology “Adjusted” PM10

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

Meteorological influence on PM2.5

Meteorological influence on PMcoarse

Influence of wind direction on PM2.5

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

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

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 -0.37 μgm-3yr-1 Urban background -0.53 μgm-3yr-1 Urban street -1.2 μgm-3yr-1 blue: raw red: adjusted

The weekly cycle of PMcoarse ambient concentrations

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%

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

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

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.