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115 Pages·2016·6.31 MB·English

POLITECNICO DI MILANO
Scuola di Ingegneria per l’Ambiente e il territorio

Enhanced modeling approach for Organic Aerosol
in the Po Valley area (Italy)

Relatore:
Prof. Ing. Giovanni Lonati
Correlatore:
Ing. Guido Pirovano
Tesi di Laurea Magistrale di:
Albertine Meroni
Matricola 805607
Anno Accademico 2014/2015
1
Index
Abstract ........................................................................................................................................................... 10
Riassunto esteso .............................................................................................................................................. 13
1 Particulate Matter ........................................................................................................................................ 16
1.1 The effects of PM on health and global warming ................................................................................. 16
1.2 Main PM sources ................................................................................................................................... 21
1.3 Methods to quantify the primary and the secondary components of PM ........................................... 23
2 VBS: the recent volatility basis set approach for modeling organic aerosol ................................................ 26
2.1 From the Odum 2 product model to VBS .............................................................................................. 26
2.2 The 1.5-Dimensional volatility basis set ................................................................................................ 34
2.3 Implementation of VBS in some case study .......................................................................................... 39
3 The modeling system .................................................................................................................................... 41
3.1 Smoke .................................................................................................................................................... 41
3.2 WRF........................................................................................................................................................ 42
3.3 CAMx ..................................................................................................................................................... 43
4 Model simulation .......................................................................................................................................... 47
4.1 The model domain ................................................................................................................................. 47
4.2 Simulation Period .................................................................................................................................. 50
4.3 CAMx inputs .......................................................................................................................................... 50
4.3.1 Meteorological inputs .................................................................................................................... 50
4.3.2 Emission inputs ............................................................................................................................... 51
4.4 Environmental Dataset .......................................................................................................................... 53
4.4.1 Meteorological Dataset .................................................................................................................. 53
4.4.2 Air Quality Dataset.......................................................................................................................... 55
4.5 Statistical indices for the validation ...................................................................................................... 57
4.6 The meteorological simulation (WRF) ................................................................................................... 58
4.6.1 Model performance evaluation ...................................................................................................... 59
2
5 The chemical simulation ............................................................................................................................... 65
5.1 CTM performance: qualitative assessment .......................................................................................... 65
5.2 CTM performance: quantitative assessment ........................................................................................ 71
6 Conclusions ................................................................................................................................................... 93
Annex 1. Results of the meteorological validation divided for regions. ......................................................... 95
Annex2. Average diurnal profiles of NO , NO , SO ....................................................................................... 102
X 2 2
Annex 3. Statistical indices used during the WRF and the CTM validations. ................................................ 103
References ..................................................................................................................................................... 107

3
Figures

Figure 1. Concentrations of PM in 2013 (Air Quality in Europe, 2014). ....................................................... 18
10
Figure 2 Concentrations of PM2.5 in 2013 (Air Quality in Europe, 2014). ...................................................... 19
Figure 3. The 2-D space. Volatility has been divided in five ranges identified with colored bands: ELVOC
(extremely low volatility organic compounds C*<3x10-4 μg m-3) LVOC (low volatility organic compounds
3x10-4<C*<0.3 μg m-3) SVOC (semi-volatile organic compounds 0.3< C*<300 μg m-3) IVOC (intermediate
volatility organic compounds 300< C* < 3x106 μg m-3) VOC (volatile organic compounds C*> 3x106 μg m-3). In
this 2-D space are placed also AMS factors: Hydrocarbon-like Organic Aerosol (HOA), Biomass-burning
Organic Aerosol (BBOA), Oxidized Organic Aerosol (OOA) with SV-OOA appearing to be less oxidized and
more volatile and LV-OOA appearing to be more oxidized and less volatile (Donahue et al., 2012). ............ 33
Figure 4. Hypothetical reaction trajectories for: SOA formation fromSVOC and IVOC ; SOA formation from
VOC (Donahue et al.,2012). ............................................................................................................................. 34
Figure 5. The four basis set in the 1.5-D VBS scheme. Hydrocarbon-like Organic Aerosol (HOA), Biomass
Burning Organic Aerosol (BBOA), anthropogenic Oxygenated Organic Aerosol (A-OOA) and Biogenic
Oxygenated Organic Aerosol (Koo et al., 2014) .............................................................................................. 35
Figure 6. The 1.5-D VBS on the two dimensional space defined by C* and average oxidation state of carbon
OS (Koo et al., 2014). ................................................................................................................................... 36
C
Figure 7. Schematic diagram of the CAMx 1.5-D VBS module (ENVIRON, 2015). ........................................... 37
Figure 8. The modelling system. ...................................................................................................................... 41
Figure 9. The three nested grids used in WRF simulation and the two nested grids (the smaller)used in
CAMx and SMOKE simulations ........................................................................................................................ 48
Figure 10. Meteorological Dataset over the Po Valley for the year 2013. ...................................................... 54
Figure 11. Location of Ispra, Milano Pascal and Bologna stations .................................................................. 56
Figure 12. View of the Po Valley from space by the MODIS sensor on board the Aqua satellite, after the
snowfall of 11 and 12 February. Source Modis. .............................................................................................. 59
Figure 13. Mixing Ratio (g kg-1) Po Valley, February 2013. .............................................................................. 60
Figure 14. Temperature (K) Po Valley, February 2013. ................................................................................... 60
Figure 15. Wind Speed (m s-1) Po Valley, February 2013. ............................................................................... 60
Figure 16. Global Radiation (W m-2) Po Valley, February 2013. ...................................................................... 61
Figure 17. Rain (mm h-1) Po Valley, February 2013. ........................................................................................ 61
4
Figure 18. Ground-level concentration predictions averaged over the entire simulation period (1-28
February 2013) for a) NO , b) NO , c) SO . Different scales are used. ............................................................ 67
X 2 2
Figure 19. Ground-level concentration predictions averaged over the entire simulation period (1-28
February 2013) for a) PM10, b) PM2.5. The same scale is used. .................................................................... 68
Figure 20. Ground-level concentration predictions averaged over the entire simulation period (1-28
February 2013) for a)EC, b) SO 2-,c) NO -,d) NH +. Different scales are used. ................................................ 69
4 3 4
Figure 21. Ground-level concentration predictions with the SOAP approach, averaged over the entire
simulation period (1-28 February 2013) for a) POA, b) SOA. Different scales are used. ................................ 70
Figure 22. Ground-level concentration predictions with the 1.5-D VBS approach, averaged over the entire
simulation period (1-28 February 2013) for a) POA, b) SOA. Different scales are used. ................................ 70
Figure 23. Comparison of model predictions (orange) with measurements (grey) of a) NO , b) NO , c) SO . 73
X 2 2
Figure 24. Comparison of model predictions (orange) with measurements (grey) of NO for Suburban and
X
Urban sites located in a) Lombardy b) Piedmont c) Veneto d) Emilia Romagna. ........................................... 74
Figure 25. Comparison of model predictions (orange) with measurements (grey) of NO for Suburban and
2
Urban sites located in a) Lombardy b) Piedmont c) Veneto d) Emilia Romagna. ........................................... 75
Figure 26. Comparison of model predictions (orange) with measurements (grey) of SO for Suburban and
2
Urban sites located in a) Lombardy b) Piedmont c) Veneto. .......................................................................... 76
Figure 27. Comparison of model predictions (orange for SOAP, green for 1.5-D VBS) with measurement
(grey) of a)PM b)PM . ................................................................................................................................ 78
10 2.5
Figure 28. Comparison of daily model predictions (red) with daily measurements (black) of Elemental
Carbon. At Milano Pascal station. ................................................................................................................... 80
Figure 29. Comparison of daily model predictions (red) with daily measurements (black) of Elemental
Carbon. At Ispra station. .................................................................................................................................. 80
Figure 30. Comparison of daily model predictions (red) with daily measurements (black) of Sulfate. At
Milano Pascal station. ...................................................................................................................................... 81
Figure 31. Comparison of daily model predictions (red) with daily measurements (black) of Sulfate. At Ispra
station. ............................................................................................................................................................. 81
Figure 32. Comparison of daily model predictions (red) with daily measurements (black) of Sulfate. At
Bologna station. ............................................................................................................................................... 81
Figure 33. Comparison of daily model predictions (red) with daily measurements (black) of Nitrate. At
Milano Pascal station. ...................................................................................................................................... 82
Figure 34. Comparison of daily model predictions (red) with daily measurements (black) of Nitrate. At Ispra
station. ............................................................................................................................................................. 82
5
Figure 35. Comparison of daily model predictions (red) with daily measurements (black) of Nitrate. At
Bologna station. ............................................................................................................................................... 82
Figure 36. Comparison of daily model predictions (red) with daily measurements (black) of Ammonium. At
Milano Pascal station. ...................................................................................................................................... 83
Figure 37. Comparison of daily model predictions (red) with daily measurements (black) of Ammonium. At
Ispra station. .................................................................................................................................................... 83
Figure 38. Comparison of daily model predictions (red) with daily measurements (black) of Ammonium. At
Bologna station. ............................................................................................................................................... 83
Figure 39. Comparison of hourly model predictions (red) with hourly measurements (black) of Sulfate. At
Bologna station. ............................................................................................................................................... 84
Figure 40. Comparison of hourly model predictions (red) with hourly measurements (black) of Nitrate. At
Bologna station. ............................................................................................................................................... 84
Figure 41. Comparison of hourly model predictions (red) with hourly measurements (black) of Ammonium.
At Bologna station. .......................................................................................................................................... 84
Figure 42. Comparison of daily model predictions (blue for SOAP, red for 1.5-D VBS) with daily
measurements (black) of Organic Carbon. At Milano Pascal station. ............................................................. 86
Figure 43. Comparison of daily model predictions (blue for SOAP, red for 1.5-D VBS) with daily
measurements (black) of Organic Carbon. At Ispra station. ........................................................................... 86
Figure 44. Comparison of daily model predictions (blue for SOAP, red for 1.5-D VBS) with daily
measurements (black) of Organic Carbon. At Bologna station. ...................................................................... 86
Figure 45. Comparison of hourly model predictions (blue for SOAP, red for 1.5-D VBS) with hourly
measurements (black) of Organic Carbon. At Bologna station. ...................................................................... 87
Figure 46. Comparison of daily model predictions (blue for SOAP, red for 1.5-D VBS) with daily Primary
Organic Aerosol (sum of PMF factors HOA, BBOA) (black) at Bologna station. .............................................. 89
Figure 47. Comparison of hourly model predictions (blue for SOAP, red for 1.5-D VBS) with hourly Primary
Organic Aerosol (sum of PMF factors HOA, BBOA) (black) at Bologna station. .............................................. 89
Figure 48. Comparison of daily model predictions (blue for SOAP, red for 1.5-D VBS) with daily PMF factor
Hidrocarbon-like Organic Aerosol (black) at Bologna station. ........................................................................ 90
Figure 49. Comparison of hourly model predictions (blue for SOAP, red for 1.5-D VBS) with hourly PMF
factor Hidrocarbon-like Organic Aerosol (black) at Bologna station. .............................................................. 90
Figure 50. Comparison of daily model predictions (blue for SOAP, red for 1.5-D VBS) with daily PMF factor
Biomass Burning Organic Aerosol (black) at Bologna station. ........................................................................ 91
Figure 51. Comparison of hourly model predictions (blue for SOAP, red for 1.5-D VBS) with hourly PMF
factor Biomass Burning Organic Aerosol (black) at Bologna station. .............................................................. 91
6
Figure 52. Comparison of daily model predictions (blue for SOAP, red for 1.5-D VBS) with daily PMF factor
Oxigenated Organic Aerosol (black) at Bologna station. ................................................................................. 92
Figure 53. Comparison of hourly model predictions (blue for SOAP, red for 1.5-D VBS) with hourly PMF
factor Oxygenated Organic Aerosol (black) at Bologna station. ..................................................................... 92
Figure 54. Mixing Ratio (g kg-1) Lombardy, February 2013. ............................................................................ 95
Figure 55. Mixing Ratio (g kg-1) Emilia Romagna, February 2013. ................................................................... 95
Figure 56. Mixing Ratio (g kg-1) Veneto, February 2013. ................................................................................. 96
Figure 57. Mixing Ratio (g kg-1) Piedmont, February 2013. ............................................................................. 96
Figure 58. Temperature (K) Lombardy, February 2013. .................................................................................. 96
Figure 59. Temperature (K) Emilia Romagna, February 2013. ........................................................................ 97
Figure 60. Temperature (K) Veneto, February 2013. ...................................................................................... 97
Figure 61. Temperature (K) Piedmont, February 2013. .................................................................................. 97
Figure 62. Wind Speed (m s-1) Lombardy, February 2013. .............................................................................. 98
Figure 63. Wind Speed (m s-1) Emilia Romagna, February 2013. .................................................................... 98
Figure 64. Wind Speed (m s-1) Veneto, February 2013. .................................................................................. 98
Figure 65. Wind Speed (m s-1) Piedmont, February 2013. .............................................................................. 99
Figure 66. Global Radiation (W m-2) Lombardy, February 2013. ..................................................................... 99
Figure 67. Global Radiation (W m-2) Emilia Romagna, February 2013. ........................................................... 99
Figure 68. Global Radiation (W m-2) Veneto, February 2013. ....................................................................... 100
Figure 69. Global Radiation (W m-2) Piedmont, February 2013. ................................................................... 100
Figure 70. Rain (mm h-1) Lombardy, February 2013. ..................................................................................... 100
Figure 71. Rain (mm h-1) Emilia Romagna, February 2013. ........................................................................... 101
Figure 72. Rain (mm h-1) Veneto, February 2013. ......................................................................................... 101
Figure 73. Average diurnal profiles of a) NO , b) NO , c) SO . ....................................................................... 102
X 2 2
7
Tables

Table 1. Air quality limit and target values, and other environmental objectives, for PM10 and PM2.5 as
given in the EU Ambient Air Quality Directive and WHO AQGs ...................................................................... 16
Table 2. Anthropogenic SOA processes with Odum 2-product scheme. ........................................................ 27
Table 3. Biogenic SOA processes with Odum 2-product scheme. ................................................................... 28
Table 4 Properties of CG/SOA pairs in SOAP module implemented in CAMx. (ENVIRON, 2015) ................... 28
Table 5. Typical oxidation and oxygenation state for the AMS factor (Donahue et al., 2012). ...................... 33
Table 6. Molecular properties of basis sets (Koo et al., 2014). ....................................................................... 38
Table 7. Implementation of VBS in some case study. ..................................................................................... 39
Table 8. SOA precursor reactions included in the CAMx SOAP module (ENVIRON, 2015). ............................ 45
Table 9. POA chemical aging scheme included in the CAMx 1.5-D VBS module (Koo et al., 2014). ............... 46
Table 10. Main parameters for the three domains ......................................................................................... 49
Table 11. Altitude and thickness of the 14 levels ............................................................................................ 49
Table 12. Input species for SOAP and for 1.5-D VBS schemes. ....................................................................... 52
Table 13. Volatility distribution factors of POA emissions. ............................................................................. 52
Table 14. Availability of hourly measurements for Meteorological ARPA dataset. ........................................ 54
Table 15. Availability of hourly measurements for Air Quality ARPA Dataset. ............................................... 55
Table 16. Mean Bias, WRF simulation. ............................................................................................................ 63
Table 17. Mean Absolute Error, WRF simulation. ........................................................................................... 63
Table 18. Index of Agreement, WRF simulation. ............................................................................................. 64
Table 19 Statistical indices for model predicted concentrations of NO , NO , SO . ....................................... 71
X 2 2
Table 20 Statistical indices for model predicted concentrations of PM , PM . ........................................... 72
10 2.5
Table 21. Statistical indices for model predicted concentrations of OCat Milano Pascal, Ispra and Bologna

stations. ........................................................................................................................................................... 87
Table 22. Statistical indices for model predicted concentrations of PMF speciatedPOA at Bologna station. 89

Table 23. Statistical indices for model predicted concentrations of NO , NO , SO ..................................... 104
X 2 2
Table 24. Statistical indices for model predicted concentrations of PM , PM ......................................... 105
10 2.5
8
Table 25. Statistical indices for model predicted concentrations of EC at Milano Pascal and Ispra stations.
....................................................................................................................................................................... 105
Table 26. Statistical indices for model predicted concentrations of SO 2-at Milano Pascal, Ispra and Bologna
4
stations. ......................................................................................................................................................... 106
Table 27. Statistical indices for model predicted concentrations of NO -at Milano Pascal, Ispra and Bologna
3
stations. ......................................................................................................................................................... 106
Table 28. Statistical indices for model predicted concentrations of NH +at Milano Pascal, Ispra and Bologna
4
stations. ......................................................................................................................................................... 106

9
Abstract
It has been established that traditional aerosol mechanisms underestimate the organic fraction of
Particulate Matter, especially due to the poorly understood formation and evolution of secondary organic
aerosol (SOA) concentrations in the atmosphere. Recent modelling studies confirm the clear lack in
reproducing the OA fraction also in the Po valley, particularly during the winter season.
Parameterizations of the traditional models and chemical aging of primary and secondary organic aerosol
are only some of the potential causes of these difficulties in reproducing OA that have been proposed.
Recently the volatility basis-set modeling approach has been developed to improve weaknesses of the
existing models that adopt simplified OA modules in which organic compounds with similar properties are
lumped together.
In this study a hybrid volatility basis set (VBS) approach the so-called 1.5 D VBS scheme implemented in the
chemical and transport model CAMx was evaluated for a one-month winter period (February 2013) over a 5
km resolution domain covering the whole Po valley.
This 1.5-D scheme uses four basis sets to describe varying degrees of oxidation in ambient OA, for freshly
emitted OA and for chemically aged oxygenated OA both from anthropogenic and biogenic sources. Both
primary and secondary organic components are assumed to be semivolatile and photochemically reactive
and are distributed in logarithmically spaced volatility bins, combining the simplicity of the 1-dimensional
VBS, that introduced the use of a volatility basis set, with the ability to describe evolution of OA in the 2-
dimensional space. Actually the 1.5-D VBS scheme adjusts oxidation state as well as volatility in response to
chemical aging by simplifying the 2-dimensional VBS model, hence it is more suitable for 3D model
applications (Koo et al.,2014).
The aim of this work is to evaluate the sensitivity of the CAMx model performance on Organic Aerosol (OA)
reconstruction with respect to the choice of the secondary OA (SOA) modeling framework, comparing the
recent 1.5D VBS algorithm (Koo et al., 2014) with the traditional Odum 2-product model (SOAP, Strader et
al., 1999).
Model performance for nitrogen oxides, sulfur dioxide and PM was evaluated using observations provided
by regional agencies for environmental protection for the whole Po valley. More specific measured data of
PM composition such as nitrate sulfate, ammonium, Elemental Carbon and Organic Carbon were available
only for three sites: the EMEP site Ispra, and the two “Supersite” Milano Pascal in Lombardy and Bologna in
Emilia Romagna. Additionally, for this latter site OA components from Positive Matrix Factorization of
Aerosol Mass Spectrometer (AMS) data performed by the CNR (National Research Council) were available
and has been used to validate the 1.5 D VBS chemical model.
As a preliminary step a careful analysis of the meteorological simulation results was carried out to
investigate the reasons for the discrepancies between model results and observations due to weather
events from those due to chemical mechanisms. February 2013 was characterized by two snowfall events
that involved the Po Valley and weren’t well reproduced by the meteorological model used. Apart from
that, overall performance of WRF was satisfactory, particularly the model reproduced temperatures, mixing
ratio and wind speed very well during all the period in the whole study area.
10