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«Dissertation zur Erlangung des akademischen Grades Doktor der Naturwissenschaften am Fachbereich Geowissenschaften der Freien Universität Berlin ...»

-- [ Page 1 ] --

Modelling the fate of secondary inorganic

aerosol and its precursors over Europe

Dissertation

zur Erlangung des akademischen Grades

Doktor der Naturwissenschaften

am Fachbereich Geowissenschaften

der Freien Universität Berlin

Vorgelegt von

Sabine Banzhaf

Mai 2014

Gutachter:

Prof. Dr. Peter Builtjes

Prof. Dr. Uwe Ulbrich

Tag der Disputation: 20.06.2014

Table of Contents

List of Figures V List of Tables IX Summary 1 Zusammenfassung 3 1 Introduction 7

1.1 Ambient aerosol size distribution........................... 8

1.2 Composition of the tropospheric aerosol over Europe................ 10

1.3 The role of SIA in air pollution and impact on climate............... 12 1.3.1 Impact on ecosystems............................. 12 1.3.2 SIA as constituent of PM........................... 13 1.3.3 Impact on climate............................... 14

1.4 SIA sources, formation and sinks........................... 15 1.4.1 Emission sources of the precursor gases.................... 15 1.4.2 Sulphate formation............................... 16 1.4.3 Nitrate formation................................ 18 1.4.4 The NH3 -HNO3 -H2 SO4 System........................ 19 1.4.5 Sinks....................................... 19

1.5 Modelling SIA concentrations and deposition fluxes................. 20

1.6 SIA mitigation in Europe............................... 22

1.7 Research aims and outline of the thesis........................ 23 1.7.1 Research aims.................................. 23 1.7.2 Outline of the thesis.............................. 26 2 Methods 27

2.1 REM-Calg

–  –  –

1.1 Observed relative chemical distribution of PM10 at the Dutch rural background site Hellendoorn. Data from Weijers et al. (2011)................... 11

1.2 Rate of aqueous-phase oxidation of dissolved sulphur dioxide by ozone (30 ppb) and hydrogen peroxide (1 ppb) as a function of solution pH at 298 K. R/L represents the rate of reaction referred to gas-phase sulphur dioxide pressure per (g m−3 ) of cloud liquid water content. Gas-aqueous equilibria are assumed for all reagents (Seinfeld and Pandis, 1998)......................... 17

1.3 Collision efficiency for the capture of particles by rain drops below the cloud as a function of particle size. Solid lines indicate model studies, while the different markers indicate laboratory studies (Warneck, 1988)................. 20

1.4 Past emission trends of nitrogen oxides (NOx ), sulphur oxides (SOx ) and ammonia (NH3 ) in the EEA-32 and EU-27 group of countries. In addition - for the EU-27

- the aggregated Member State 2010 and 2020 emission ceilings for the respective pollutants are shown.

Data source: National emissions reported to the LRTAP Convention provided by the UNECE, provided by the European Environment Agency (EEA) in January 2014............................................ 23

3.1 Influence of pH variation on model wet deposition flux for SO2 (∗) and NH3 (+). 49

3.2 Model Domain and locations of all used UBA wet deposition flux measurement sites (.) and UBA air concentration measurement sites (o) listed in Table 1.... 52

3.3 TRAMPER monthly precipitation sums for July 2005 (a) and January 2005 (b) compared to observations at 17 (July) respectively 15 (January) UBA stations.. 54

3.4 Vertical distribution of domain average sulphate air concentration for (a) Case 1 and (c) Case 3 (vertical lines show maximum values of Case 1) and deviation of the domain wet deposition sum from the base run for (b) Case 2 and (d) Case 3 of the different droplet pH runs for the investigation period............. 55 3.5 (a) Modelled and observed daily mean surface sulphate air concentrations at UBA station Melpitz and (b) modelled versus observed NHx wet deposition sums at 17 UBA stations spread over Germany.......................... 56

–  –  –

4.1 Spatial distribution of the total annual (a) SOx, (b) NOx and (c) NH3 emissions in t a−1 cell−1...................................... 80

4.2 Profiles of (a) monthly and (b) hourly ammonia emission factors applied in RCG. 81

4.3 Map of observational station locations: PM10 concentrations (rural background) (x), PM10 concentrations (suburban background) (+), SIA concentrations ([]), wet deposition fluxes (o)................................... 83 Daily mean PM10 concentrations in µg m−3 on (a) 4 and (b) 5 April 2009 and (c) 4.4 12 and (d) 13 April 2009 derived by Optimal Interpolation of observations..... 85

4.5 Daily mean modelled PM10 concentrations (a) versus observations at 42 AirBase sites and (b) versus observations at AirBase site Westerwald-Herdorf........ 86





4.6 Daily mean modelled SIA concentrations (a) versus observations at 3 RIVM sites and (b) versus observations at station Kollumerwaard................ 87

4.7 Modelled wet deposition sum of (a) SOx, (b) NOy, (c) NHx and (d) total precipitation for the investigation period versus observations at 11 UBA sites....... 88

4.8 Modelled mean SIA concentration of the base run for (a) 4 April 2009 and (b) 13 April 2009........................................ 90

4.9 Daily mean (a) sulfate, (b) nitrate and (c) ammonium concentration at station Westerwald-Herdorf for different ammonia emission scenario runs.......... 91

4.10 Mean modelled (a) SIA concentration and (b) total deposition flux of the base run to the left of the dashed line and mean change in (a) SIA concentration and (b) total deposition flux for different emission scenario runs to the right of the dashed line. (a, b) show results for scenario runs on the German domain excluding the boundary conditions and (c, d) those on the German domain including the boundary conditions. The mean refers to the average over the investigation period from 24 March to 28 April............................... 93 m−3 ) 4.11 (a) Base case mean SIA concentration( µg of the investigation period and (b) absolute and (c) relative SIA reduction by means of the −40 %NH3 GD scenario run compared to the base case............................. 96

4.12 Response of (a) the mean modelled SIA concentration and (b) the mean modelled SOx, NOy and NHx total deposition in the German domain to ammonia emission changes using a variable (solid line) or constant (dashed line) droplet pH..... 97

5.1 Emission trends of (a) SO2, NOx and NH3 in the EU-27+ member States and (b) SO2 and NOx in International Shipping for 1990 to 2010 in % with 1990 as reference. The thin lines show the average trend computed over the entire period, the decrease per year is displayed as text....................... 119

5.2 Mean 60 days moving average of (a) temperature, (b) relative humidity, (c) windspeed and (d) precipitation at 66, 61, 59 and 66 German observational sites, respectively, from 1990-2009.............................. 126

5.3 Mean 60 days moving average (left panel) and seasonal cycle (right panel) of (ab) SO2, (c-d) SO4, (e-f) NO2, (g-h) TNO3 and (i-j) TNH4 for the time period 1990-2009. The number of considered stations is given in the figure captions... 129

5.4 Boxplots of the absolute (left panel) and relative (right panel) observed (blue) and modelled (red) trends for the considered components and time periods....... 133

5.5 Scatter plots of the observed versus modelled trends for the studied components at the considered stations for the three different time periods. At each individual station the marker (described in the legend on the top right of the plot) indicates if the observed and/or modelled trend is significant following the Mann-Kendall test at a 95% confidence level.............................. 135 Observed (blue) and modelled (red) annual mean (crosses), 5th percentile (squares) 5.6 and 95th percentile (triangles) and corresponding trend line of (a) SO4, (b) TNO3 and (c) TNH4. Solid lines indicate a significant and dashed lines a non-significant trend........................................... 137 5.7 (a) Observed and (b) modelled annual average concentrations of SO2, NO2, SO4, TNO3 and TNH4 relative to annual average concentrations of 1990 (solid lines) at 23, 37, 15, 9 and 7 European stations, respectively, for the years 1990, 1995, 2000, 2005 and 2009. Trends (in % a−1 ) and the corresponding trend lines (dashed lines) are given for each component.......................... 140

5.8 Amount of (a) sulphate, (c) ammonium and (d) nitrate (solid lines) formed from 10 ktons of SO2, NH3 and NO2 emissions, respectively, relative to the amount formed in 1990, for the different labels as indicated by the colors, for the entire time period 1990 to 2009. Panel (b) shows the resultant SO2 per unit SO2 emission for each label for the 1990 to 2009 time period. The corresponding trend lines are presented as dashed lines. The dots denote results for the runs forced with 2005 meteorology........................................ 141

5.9 Locations of the observational sites used for the analysis of the different components for the 1990 to 2009 time period......................... 157

5.10 As Figure 5.9 for the 1995 to 2009 time period.................... 158

5.11 As Figure 5.9 for the 2000 to 2009 time period.................... 159

5.12 Locations of the observational sites used for the analysis of the different components when considering only those stations that pass the data selection criteria for all three periods..................................... 160 List of Tables

1.1 Illustration of ambient particle fractions adopted from McMurry et al. (2004)... 9

–  –  –

5.1 Number of stations of the applied observational dataset per component and time period before and after the visual screening of the observed time series....... 122

5.2 Statistical comparison between measured and modelled meteorological parameters using daily observations at European observational sites. The number of considered stations, mean correlation, observed mean, RMSE and bias are given..... 125

–  –  –

Secondary inorganic aerosol (SIA), which are sulphate, nitrate and ammonium, is involved in the eutrophication and acidification of ecosystems, the formation of health relevant particulate matter (PM) and climate change by affecting the radiation balance of the earth.

A thorough understanding of the SIA budget is of scientific interest and is required to be able to devise emission mitigation strategies that are effective for biodiversity, climate change and human health. SIA concentrations and the deposition of sulphur and nitrogen compounds show non-linear responses to emission changes of sulphur dioxide, nitrogen oxides and ammonia. At the start of this study CTMs did not incorporate all processes leading to these non-linearities and previous studies highlighted that the performance of these models showed clear deficiencies in comparison to observations. Within the here presented study the process description of SIA formation and wet removal is improved within two state-of-the-art regional CTMs followed by a comprehensive operational and dynamic evaluation of the models against observations with the focus on the non-linearity aspects.

The chemical transport model REM-Calgrid (RCG) has been improved by the implementation of pH dependent aqueous-phase sulphate formation. Furthermore, a new wet deposition scheme including cloud liquid water content dependent in-cloud scavenging and pH dependent droplet saturation has been incorporated. A model sensitivity study varying cloud and rain droplet pH within atmospheric ranges has revealed a significant impact on resultant SIA air concentrations and wet deposition fluxes of sulphur and nitrogen compounds. It was found that the effect on sulphate formation in clouds in between precipitation events and prior to rain out dominates the impact of pH variations. Furthermore, a model run using modelled droplet pH has been performed and compared to a model run applying a constant pH of 5 and to observations. The results have revealed that using a modelled droplet pH is preferable to using a constant pH leading to an increased model performance and better consistency concerning air concentrations and wet deposition fluxes.

Using the improved RCG model system two PM episodes over Central Europe characterized by a high SIA contribution have been analysed. The model performed well in capturing the temporal variation of the SIA concentrations and was successfully used to analyse the origin, development and characteristics of the episodes. To investigate the response of SIA concentrations to precursor emission changes several model runs for different emission scenarios varying emissions of sulphur dioxide, nitrogen dioxide and ammonia have been performed. The results confirmed that the response of SIA concentrations and sulphur and nitrogen deposition fluxes to precursor emission changes is non-linear.



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