1 Non-linear effects in modelling PM 10 and PM 2,5 contributions from anthropogenic sources Clemens Mensink, Felix Deutsch, Jean Vankerkom and Liliane Janssen VITO Centre for Integrated Environmental Studies Mol, Belgium

2 1.Introduction 2.Methodology EUROS model Extension to PM modelling 3.Results & discussion Emission scenarios for 2010 Sector contributions Non-linear effects 4.Conclusions Contents

3 Introduction In Belgium and Europe we are faced with episodes of high PM concentrations (2003: 10 episodes with PM 10 > 100 µg/m³) These are associated with adverse health effects (Pope et al., 1995; Dockery et al., 1993) We want to study why and how these episodes occur, using numerical models What are the possible abatement strategies ? (policy support)

4 EUROS model, originally developed for ozone at RIVM and implemented in Belgium in 2001 as an operational tool for policy support (BelEUROS) 2004/2005: extension for PM modelling  Domain: Europe (shifted pole coordinates)  Resolution: horizontal: 60  15 km; vertical: 4 layers  Meteorology: ECMWF (T, rH, ws+wd, CC, PR), ALADIN (mixing height)  Emissions: EMEP/CORINAIR for Europe + detailed national emission inventories Methodology

5 PM 10 modelled for 2002

6 wet surface layer mixing layer reservoir layer upper layer wet deposition fumigation vertical diffusion deposition dry time

7 Implementation of two new modules in the ozone-version of BelEUROS gas phase mechanism: CB-IV (ozone) -> CACM (PM) ( Caltech Atmospheric Chemistry Mechanism) 122 components, 361 reactions contains ozone-chemistry AND describes the formation of presursors for secondary organic aerosols in a mechanistic way aerosol module: MADRID 2 (Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution) Methodology

8 anorganic compounds organic hydrophilic compounds organic hydrophobic compounds emissions liquid phase mechanism heterogenic phase mechanism INORG. EQUIL. emissions prim. inorg. aerosols, NH 3 ORGANIC HYDROPHIL. ORGANIC HYDROPHOB. Nucleation Condensation emissions prim. org. aerosols  iterations till LWC = const. lumped (5c.) Lumped (5c.) Inorg. + Organ. composition; size distribution inorg. distr., LWC, H + org. distr., new LWC/H + total distrib. inorg. + org. CHEMICAL MECHANISM EQUILIBRIUM MODULE PARTICLE FORMATION Chemical-Aerosol-Module CACM/MADRID 2 SO 2, NOx NMVOS NMVOC

9 IndustryTraffic Agriculture

10 Biogenic vegetation: isoprenes and terpenes NOT: mineral dust from outside the domain

11 Biogenic NOT: forest fires NOT: sea salt

12 6-hourly reanalysed meteorological fields (ECMWF)

13 Mixing height for the domain (ALADIN)

14  mass and 7 chemical compounds for 2 size bins (PM 10-2,5, PM 2,5 ):  primary inorganic compounds  primary organic compounds  elementary carbon  sulphate SO 4 2- (primary and secondary)  ammonium NH 4 +, nitrate NO 3 - and SOA Model output

15 Measured & modelled PM 10 - URBAN station

16 Measured & modelled PM 10 - URBAN station

17 Results & Discussion Emission scenarios for 2010 Base year 2002 (EMEP) CAFE 2010 CLE scenario (IIASA) Setting all emissions in Flanders to zero Sector contributions Setting all emissions in sector to zero Non-linear effects

18 Belgian emissions for 2002 Belgian emissions (in Mg) for the year 2002 according to EMEP, Expert emissions W-05emis02-V5 ( )

19 Results & Discussion 2002CLE 2010

20 Results & Discussion

21 Results & Discussion

22 Concentration change Relative difference of PM 10 - and PM 2,5 -concentrations between 2002 and 2010 in the Belgian regions.

23 Sector contributions in 2010

24 Sector contributions Relative contributions [%] of the anthropogenic sources per sector to PM10- and PM2.5-concentrations obtained by setting all anthropogenic emissions in one sector in Flanders to zero.

25 Reductions for zero emissions Reductions [%] in PM 10 - and PM 2,5 -concentrations obtained by setting all anthropogenic emissions in Flanders to zero in 2010.

26 Results & Discussion Reduction in PM 10 obtained when setting all emissions in Flanders to zero: 15,2% Sum of the reductions in PM 10 obtained when setting all emissions in individual sectors to zero: 13,8% Did we miss something (1,4%) ??? No!  non-linear effects (or synergies) account for approximately 10% of the reductions !!!

27 Results & Discussion Belgian emissions (in Mg) for the year 2002 according to EMEP, Expert emissions W-05emis02-V5 ( )

28 Discussion: non-linear effects Emissions from various sectors are effectively necessary for the formation of the secondary compound (e.g. formation of ammonium-nitrate through emission contributions from the sectors traffic (NO x ) and agriculture (NH 3 )) During the formation of secondary aerosols (nitrate, sulphate, ammonium and SOA) a small reduction in a gaseous compound (e.g. SO 2 ) does not necessarily lead to the same amount of reduction of the secondary compound (e.g. SO 4 ) Non-uniform spatial distribution of the emissions and concentrations may locally lead to enhanced formation of secondary aerosols

29 Assumptions & caveats The emission factor used to estimate the emissions for the sector “agriculture” is very uncertain (and currently in revision). Diffusive emission sources (e.g. fugitive emissions stemming from handling and storing activities) are not taken into account, although recently they are gaining more importance in abatement strategies with the aim to comply with the limit values for particulate matter (EU directive 1999/30/EU) The CLE scenario was still in discussion in the context of CAFE and can therefore not be considered as the definite choice.

30 EUROS has been extended with CACM & MADRID 2 to model mass and chemical composition (7 compounds) of PM 2,5 and PM 10-2,5 Contributions from anthropogenic sources in Flanders are found to be responsible for 17,1% of the annual averaged PM 10 concentrations in Flanders in In 2010 this contribution drops to 15,2%. Non-linear effects can not be neglected and were found to be in the order of 10%. The results demonstrate the severe limitations with respect to impact of national policy measures for relatively small countries such as Belgium Conclusions

31 We would like to thank the Flemish Environmental Administration for financially supporting this study We would like to thank the Flemish Environmental Agency and the Interregional Cell for the Environment in Brussels for supplying the air quality data Acknowledgement