A Study of Aerosol Optical and Physical Properties in Sydney, Australia
by
Ghassan Taha
A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN THE FACULTY OF SCIENCE AND TECHNOLOGY THE UNIVERSITY OF NEW SOUTH WALES AUSTRALIA The University of New South Wales July 2000 CERTIFICATE OF ORIGINALITY
I hereby declare that dlis submission is my own work and to die best of my knowledge it contains no materials Jl='iously published or wriaa by another pers011, nor material which to a subslantial extent bas been accepted for the award of any other degree or mploma at UNSW or any o1her ed11caricmal iDsti1Uliou, except where due aclcnowlcdgement is made in tbe thesis. Any COlllribulion made to die ~ by others, with whom I have walked at UNSW or elsewbeR, is explicitly acknowledged in the thesis.
I also declare that die infdlCCIUal COllfellt of dlis thesis is die product of my own work, except to die ex1a11 that assistance mm others in die project's design and conception or in style, piesentatiOD and linguistic expression is acknowledged. Acknowledgments
I would like to express my deep gratitude to my supervisor Dr. Gail Box of the School of Physics, University of New South Wales, for her continuous support and help, and especially for her patient proof reading of my thesis. I also like to acknowledge the assistance and advice, given by my co-supervisor Dr Michael Box. I am deeply grateful to both of them, for introducing me to this exciting field, sending me to various conferences and workshops (as well as paying for my expenses during these trips), and for introducing me to many leading scientist in this field (inside and outside Australia). Their support kept me going throughout the project, with utmost interest and passion. I would also like to acknowledge the help given by Dr David Cohen of the Australian Nuclear Science and Technology Organization, (ANSTO), for his supervision of the work I did with ANSTO (presented in Chapter 3). His enthusiastic encouragements and challenging discussions always motivated me to even work harder in search of answers. I also acknowledge ANSTO 3 MV accelerator staff, in particular Ed Stelcer for his assistance with the elemental analysis and black carbon measurements. His help was always offered cheerfully without hesitation. I also acknowledge AINSE for their financial support for this aspect of the study (absorption measurements), and Botany council for providing the Mascot aerosol measurements. I must thank Dr. John Gras of the CSIRO, Dept. of Atmospheric Research, Aerosol Laboratory, for hosting me in 1997, and for his help and advice. He introduced me to the thermodynamics of aerosol particles (Chapter 4), and helped me to understand the chemistry of aerosol and other various aspects of aerosol in situ measurements. This visit was thankfully funded by the Australian Academy of Science. I would like to express my sincere appreciation to Dr Michael King for providing his computer codes, and Dr. P. Saxena for providing us SCAPE2 model. I also acknowledge the help given by the NSW EPA, Air Quality Studies Section, who provided the nephelometer and TEOM measurements, as well as other meteorological parameters. Thanks to the Ozone Processing Team of NASA / Goddard Space Flight Center, for making ADEOS / TOMS and EP / TOMS overpass total ozone measurement available from their internet site http://jwocky.gsfc.nasa.gov/adeos/. ii
Technical help was also given by the computing unit, mainly Kristien and David, electronic workshop, mainly Mark, and mechanical workshop, Eddy and Peter, who helped installing the MFRSR. I deeply appreciate their help and support, which was always given without hesitation. Thanks also to Yankee Environmental Systems for designing and building the MFRSR and Mark Beaubien for his help. My gratitude to all my friends, inside and outside the School of Physics, and fellow postgraduate students, for their support and encouragements. The weekly soccer game organised by some staffs and students of the school, the Sunday soccer game organised my friends outside the school, and the weekly beach volleyball, organised by astrophysics people, were major social events that helped me going throughout my project. Thanks to Yoshi Iinuma, for many useful discussions, being the only other student who is doing similar work. Finally, I would like to dedicate this thesis to my parents who always took care of me, and helped pushing me to where I am now. I could not do it without their support and guidance throughout my life. Special thanks to all my brothers and sister too. Abstract
In this study, we investigate the aerosol optical and physical properties in Sydney, using both ground-based remote sensing and in situ methods. Continuous observations were carried out, using a Multifilter Rotating Shadowband Radiometer (MFRSR), where simultaneous measurements of global, diffuse and direct components of solar irradiance were collected daily at one broadband, and six narrowband channels. Information about aerosol optical thickness was obtained by removing the molecular and gaseous contributions, and the total column size distribution of aerosol particles was calculated using constrained linear inversion. The size distribution is used to calculate total column mass of aerosols. The diffuse to direct ratio is used to determine the index of absorption for aerosol particles. In situ methods included measurements of aerosol elemental composition, mass concentration, scattering, and absorption coefficients. Accelerator Ion Beam Analysis (IBA) elemental composition, and an equilibrium model for inorganic multi-component atmospheric aerosol (SCAPE2) were used to estimate the particle growth and a value of enhancement growth factor. Both molar fraction and mass fraction approaches were used to calculate refractive index and density of particles. And empirical models were used to determine values these parameters as a function of relative humidity. An intensive study to test and validate the Laser Integrating Plate Method (LIPM) of determining absorption coefficient was carried out. Results show that the value of mass absorption coefficient c = 10 m2g· 1 previously used for mass determination, and widely accepted for diesel fuel emission, is an inappropriate choice. More accurate estimates of c =7 and 6 m2g" 1 for soot and ambient aerosol particles were experimentally determined. The new values were in good agreement with the theoretically determined values. Eigenvalue analysis is used for inferring total ozone column and aerosol optical thickness from spectral measurements. Results of inferred column ozone showed an excellent agreement with satellite ozone measurements, obtained by ADEOS and EP/TOMS instruments. The mean percent difference with TOMS measurements was 1 % iv and 2 %. Eigenvalue analysis was also applied to the problem of determining water vapour column, using radiometer measurements. Long-term measurements were used to study the diurnal and temporal variability of aerosol properties, as well as ozone and water vapour column abundances. V
Pu bi ications
Refereed Publications Taha, G., and G. P. Box, "New method for inferring total ozone and aerosol optical thickness from multispectral extinction measurements using eigenvalue analysis" Journal ofGeophysical Research Letters, 26, pp. 3085-3088, 1999. Taha, G, D. Cohen, E. Stelcer, and G. Box, "Black carbon measurement from Teflon filters using Laser Integrating Plate Method (LIPM)", in preparation. Referred Conference Proceedings Taha, G. and G. Box, "Multispectral radiometer monitoring of aerosol in Sydney'', Proceeding 14th international clean air & environment conference, Melbourne, Vic., pp. 480-485,1998. Taha, G. and G. Box, "A study of aerosol optical and physical properties using a multispectral radiometer in Sydney", 15th international clean air & environment conference, Sydney, submitted, 2000. Cohen, D., G. Taha, E. Stelcer, D. Garton, and G. Box, "The measurement and sources of fine particle elemental carbon or soot at several key sites in NSW over the past eight years". 15th international clean air & environment conference, Sydney, submitted, 2000. Conference Presentations Box G., and G. Taha, "Monitoring of aerosol optical properties in Sydney", 3rd National AMOS Conference ofthe Australian Meteorological and Oceanographic Society, University of Tasmania, Hobart, Feb. 1996. Taha, G., and G. Box, "Air quality monitoring in Sydney", 4th National AMOS Conference ofthe Australian Meteorological and Oceanographic Society, Macquarie University, Sydney, Feb. 1997. Taha, G., and G. Box, "Atmospheric aerosol optical and physical properties using multifilter rotating shadowband radiometer in Sydney, Australia", The 22nd General Assembly ofthe International Union of Geodesy and Geophysics (IUGG99), University of Birmingham, UK 18th to 30th July 1999. vi
Taha, G., and G. Box, "Ground-based remote sensing of aerosol particles using multispectral radiometer", ?1h National AMOS Conference ofthe Australian Meteorological and Oceanographic Society, Melbourne University, Melbourne, Feb. 2000. vii
Table Of Contents
Acknowledgments ...... i Abstract ...... iii Publications ...... v List of Figures ...... xii List of Tables ...... xix Introduction ...... 1 1.1 Overview ...... 1 1.2 Objectives ...... 2 1.3 Thesis outline ...... 4 Aerosol Optical Thickness Inferred From Spectral Radiometer Measurements ...... 6 2.1 Introduction ...... 6 2.2 Instrumentation ...... 7 2.2.1 Multifilter Rotating Shadow band Radiometer (MFRSR) ...... 7 2.2.1.1 Angular corrections ...... 10 2.2.1.2 Langley analysis ...... 12 2.2.1.3 Junge coefficient ...... 15 2.2.2 PTBlO0A analogue barometer ...... 15 2.2.3 Surface measurements ...... 16 2.2.3.1 Belfort Model 1590 Nephelometer ...... 16 2.2.3.2 PMlO Tapered Element Oscillating Microbalance (TEOM) ...... 16 2.3 Results and discussion ...... 16 2.3.1 Temporal variation of aerosol optical thickness ...... 20 2.3.2 Temporal variation of Junge coefficient ...... 26 2.3.3 Diurnal variation of aerosol optical thickness ...... 26 2.3.4 Comparison of column and surface measurements ...... 29 2.3.4.1 MFRSR and Nephelometer ...... 29 2.3.4.2 MFRSR and TEOM ...... 31 viii
2.3.4.3 Temporal variation ...... 32 2.3.4.3.1 Correlation of Nephelometer vs. TEOM ...... 32 2.3.4.3.2 Determination of effective scale height ...... 40 2.3 .4.4 Diurnal variation ...... 44 2.4 Summary and conclusion ...... 49 Aerosol Light Absorption Measurements ...... 50 3.1 Introduction ...... 50 3 .2 Theory and calculation ...... 51 3.2.1 Mie calculation theory ...... 51 3.2.1.1 Complex refractive index and absorption of electromagnetic energy ... 51 3 .2.1.2 Absorption of electromagnetic energy ...... 53 3 .2.1.3 Mie scattering for a single sphere ...... 54 3 .2.1.4 Attenuation extinction coefficient ...... 55 3.3 Experimental measurements ...... 56 3.3.1 PM2.5 sampler ...... 56 3.3.2 Analysis techniques ...... 58 3.3.2.1 Accelerator based Ion Beam Analysis (IBA) ...... 58 3.3.2.1.1 PIXE ...... 58 3.3.2.1.2 PESA ...... 61 3.3.2.1.3 PIGME ...... 61 3.3.2.2 Determination of aerosol types ...... 61 Sulfate ...... 61 Soil ...... 64 Organic carbon ...... 64 Salt ...... 64 Smoke ...... 64 Black Carbon ...... 64 Reconstructed fine mass ...... 65 3.3.3 Laser Integrating Plate Method (LIPM) ...... 65 3.3.4 Smoke Stain Reflectometer ...... 67 3.4 Testing methods and measurements ...... 68 ix
3.4.1 Calibration for transmission measurements ...... 70 3.4.1.1 LIPM measurements using black carbon particles ...... 71 3.4.1.2 LIPM vs. Reflectometer ...... 72 3.4.2 Theoretical calculation of mass absorption coefficient...... 78 3 .4.2.1 Particles refractive index and density calculation ...... 78 3.4.2.2 Standard black carbon samples ...... 80 3.4.2.3 Measurements of absorption coefficient of aerosol ...... 80 3.4.2.3.1 Experimental measurements ...... 80 3.4.2.3.2 Theoretical calculation ...... 83 3.4.2.3.3 Validation ...... 89 3.4.2.3.4 LIPM vs. Reflectometer ...... 92 3.4.2.3.5 Possible overestimation ...... 92 3.5 Summary and conclusion ...... 94 The Use Of A Thermodynamic Model In Predicting Aerosol Growth Factor ...... 96 4.1 Introduction ...... 96 4.2 Experimental measurements ...... 97 Fine particles mass measurements at Mascot ...... 97 4.3 Thermodynamic equilibrium model...... 98 4.3.1 Theory of equilibrium model ...... 98 4.3.2 Chemical potentials and equilibrium constants ...... 98 4.3.3 Activity coefficients ...... 100 4.3.4 Water activity ...... 102 4.4 Application of the model...... 104
4.4.1 The NaCl, NH3, H2SO4, HNO3,H2O system ...... 104 4.4.2 Computational algorithm ...... 105 Input values ...... 110 4.5 Refractive index and molar fraction ...... 110 Prediction of mean real refractive index of urban aerosol ...... 110 4.6 Results and discussion ...... 112 4.6.1 Prediction of aerosol particle growth ...... 112 X
4.6.2 Prediction ofreal refractive index and density of ambient aerosol...... 114 4.7 Summary and conclusion ...... 118 Size Distribution Of Aerosol Particles ...... 120 5.1 Introduction ...... 120 5 .2 Constrained linear inversion ...... 121 5.2.1 Solution description ...... 121 5.2.2 Application of the technique to the inversion of aerosol size distribution problem ...... 123 5.3 Analytic Eigenfunction theory ...... 125 5.4 Testing procedure ...... 128 5.4.1 Constrained linear ...... 130 5.4.2 Analytic eigenfunction theory ...... 133 5.4.3 Effect of adding extra information ...... 133 5.5 Results and discussion ...... 136 5.5.1 Aerosol size distribution results ...... 138 5.5.2 Junge coefficient ...... 151 5.5.3 Effect ofrelative humidity on aerosol size distribution ...... 154 5.6 Summary and conclusion ...... 160 Determination Of Index Of Absorption Using MFRSR ...... 162 6.1 Introduction ...... 162 6.2 Theoretical calculation ...... 163 6.2.1 Diffuse to Direct method ...... 163 6.2.2 Radiative transfer equation ...... 167 6.2.3 Radiative characteristics ...... 168 6.2.3.1 Phase function and Asymmetry parameter ...... 169 6.2.3.2 Aerosol size distribution ...... 170 6.2.4 Determination of index of absorption and ground reflectivity ...... 171 6.3 Results and discussion ...... 172 6.3.1 Theoretical calculations ...... 172 6.3.2 Experimental results ...... 176 6.4 Summary and conclusion ...... 187 xi
Inferring Total Ozone And Aerosol Optical Thickness Using Eigenvalue Analysis ...... 189 7 .1 Introduction ...... 189 7 .2 Theory ...... 190 7.2.1 Determination of ozone optical thickness and the prediction of 0.614 µm measurements ...... 190 7 .2.2 Eigenvalue analysis ...... 190 7.2.2.1 Prediction of0.615 measurements ...... 193 7.2.2.2 King and Byrne method ...... 195 7 .3 Results and discussion ...... 195 7.3.1 Comparison with TOMS ...... 196 7.4 Summary and conclusion ...... 203 Water Vapour Column Abundance Retrieval In the 0.936 µm Region ...... 206 8.1 Introduction ...... 206 8.2 Methodology ...... 207 8.2.1 Modified Langley Plot ...... 207 8.2.1.1 Prediction of aerosol optical thickness ...... 208 Eigenvalue analysis ...... 208 Quadratic form ...... 208 8.2.2 Water vapour transmittance model ...... 210 Parameterisation of water vapour transmittance ...... 210 8.3 Results ...... 217 8.3.1 Column water vapour retrieval...... 217 8.4 Summary and conclusion ...... 220 Conclusion ...... 225 9 .1 Future work and recommendations ...... 227 Bibliography ...... 229 Appendix A ...... 229 xii
List of Figures
Figure 2.1: Schematic diagram of the Multifilter Rotating Shadowband Radiometer, MFRSR, and its geometry ...... 8 Figure 2.2: Photo of the MFRSR ...... 8 Figure 2.3: Filters spectral response of MFRSR and the atmospheric transmittance from LOWTRAN 7, Midlatitude Summer, at (f zenith angle ...... 11 Figure 2.4: Angular response error ofMFRSR angle from south to north ...... 13 Figure 2.5: Angular response error ofMFRSR angle from west to east...... 13 Figure 2.6: Observed irradiance components of (global horizontal, diffuse horizontal and direct normal), March 19, 1997 ...... 14 Figure 2.7: Langley regression plot for (the 5 narrow-band direct-normal solar irradiance, in W/m2/nm),for the morning of March 19 1997 ...... 14
2 Figure 2.8: Time series of the MFRSR calibration constant, 10 (W/m /nm), at A=415.5 nm ...... 17
2 Figure 2.9: Time series of the MFRSR calibration constant, 10 (W/m /nm), at A.=501.7 nm ...... 17
2 Figure 2.10: Time series of the MFRSR calibration constant, 10 (W/m /nm), at A.=615.5 nm ...... 18
2 Figure 2.11: Time series of the MFRSR calibration constant, 10 (W/m /nm), at A.=672.7 nm ...... 18
2 Figure 2.12: Time series of the MFRSR calibration constant, 10 (W/m /nm), at J..=869.7 nm ...... 19 Figure 2.13: Temporal variation of aerosol optical thickness, 'taer(A), as measured by the MFRSR from Dec. 16 1995 to Sept. 16 1997, during the morning period ...... 21 Figure 2.14:Temporal variation of aerosol optical thickness, 'taer(A), as measured by the MFRSR from Dec. 16 1995 to Sept. 16 1997, during the afternoon period ...... 22 Figure 2.15: Monthly mean value of the aerosol optical thickness, 'taer(A),A.=416 nm ..... 23 Figure 2.16: Monthly mean value of the aerosol optical thickness, 'taer(A),A.=502 nm ..... 23 Figure 2.17: Monthly mean value of the aerosol optical thickness, 'taer(A),A.=616 nm ..... 24 xiii
Figure 2.18: Monthly mean value ofthe aerosol optical thickness, 't'aerCA),A.=673 nm ..... 24 Figure 2.19: Monthly mean value of the aerosol optical thickness, 't'aer(A),A.=870 nm ..... 25 Figure 2.20: Time series of the calculated Junge coefficient, v*, at the morning, from September 25 1996, to September 16 1997 ...... 28 Figure 2.21: Time series of the calculated Junge coefficient, v*, at the afternoon, from September 25 1996, to September 16 1997 ...... 28 Figure 2.22: Diurnal variation of aerosol optical thickness, 't'aer(A ), on March 19 1997 ... 30 Figure 2.23: Diurnal variation of aerosol optical thickness, 't'aer(A ), on April 30 1997 ..... 30 Figure 2.24: The morning daily average of the aerosol optical thickness, A=502nm ...... 33 Figure 2.25: The morning daily average of the aerosol nephelometer scattering coefficient...... 33 Figure 2.26: The morning daily average of the dry aerosol mass concentration (TEOM) ...... 34 Figure 2.27: The morning daily average of the calculated total mass of particles ...... 34 Figure 2.28: The afternoon daily average of the aerosol optical thickness, A=502nm ..... 35 Figure 2.29: The afternoon daily average of the aerosol scattering coefficient...... 35 Figure 2.30: The afternoon daily average of the dry aerosol mass concentration ...... 36 Figure 2.31: The afternoon daily average of the calculated total mass of particles ...... 36 Figure 2.32: Relationship between the morning average of the aerosol particle scattering coefficient, and mass concentration ...... 38 Figure 2.33: Relationship between the afternoon average of the aerosol particle scattering coefficient, and mass concentration ...... 39 Figure 2.34: Time series of the effective scale height HeJJ(km), estimated using scattering measurements ...... 41 Figure 2.35: Time series of the effective scale height Heff (km), estimated using mass measurements ...... 41 Figure 2.36: Relationship between the morning effective scale heights HeJJ(km) estimated using scattering measurements and mass measurements ...... 42 Figure 2.37: Relationship between the afternoon effective scale heights Heff (km) estimated using scattering measurements and mass measurements ...... 42 xiv
Figure 2.38: Diurnal variation of raer' /Jsp(dry and ambient), Heffi March 19 1997 ...... 45
Figure 2.39: Diurnal variation of raer' /Jsp(dry and ambient), Heffi May 13 1997 ...... 45
Figure 2.40: Diurnal variation of raer' /Jsp(dry and ambient), Heffi October 30 1996 ...... 47
Figure 2.41: Diurnal variation of ~er' /Jsp(dry and ambient), Heffi April 4 1997 ...... 47
Figure 2.42: Diurnal variation of r , /Jsp(dry and ambient), He«, March 20 1997 ...... 48 aer -JJ' Figure 3.1: A schematic diagram of the sampling unit PM2.5 mm...... 57 Figure 3.2: Energy of Ka, La, and Ma X-Ray as a function of atomic number...... 59 Figure 3.3 (a): Typical PIXE spectrum for Teflon filter, Mascot July 9, 1997 ...... 60 Figure 3.4 (a): Typical PESA spectrum for Teflon filter Mascot July 9, 1997 ...... 62 Figure 3.5 (a): PIGME spectrum for Teflon filter, Mascot July 9, 1997 ...... 63 Figure 3.6 (a): Schematic diagram of Laser Integrating Plate System, (LIPM) ...... 66 Figure 3.7: Photo of the EEL smoke stain reflectometer, model 43D ...... 69 Figure 3.8: Scanning electron microscope (SEM) photographs for 3 carbon types ...... 73 Figure 3.9: Estimated black carbon for the candle experiment, Teflon filters ...... 74 Figure 3.10: Estimated black carbon for the acetylene experiment, Teflon filters ...... 74 Figure 3.11: Estimated black carbon for the graphite experiment, Teflon filters ...... 75 Figure 3.12: Estimated black carbon for the candle experiment Nuclepore filters ...... 75 Figure 3.13: ln[Io/1] of the LIPM vs. ln[Ro/R] of the reflectometer, for candle and acetylene (Teflon) ...... 76 Figure 3.14: ln[Io/1] of the LIPM vs. ln[Ro/R] of the reflectometer for candle and acetylene (Nuclepore) ...... 76 Figure 3.15: ln[Io/1] of the LIPM vs. ln[Ro/R] of the reflectometer, for graphite (Teflon) ...... 77 Figure 3 .16: Specific mass absorption coefficient E of spherical particles at A.=630 nm .. 82 Figure 3.17: Estimated elemental carbon vs. ln[Io/1] for Mascot area during 1997 (all) .. 85 Figure 3.18: Estimated elemental carbon vs. ln[Io/1] for Mascot area during 1997 (Wed) ...... 86 Figure 3.19: Estimated elemental carbon vs. ln[Io/1] for Mascot area during 1997 (Sun) ...... 86 Figure 3.20: Modelled specific mass absorption coefficient Eat A.=630 nm ...... 87 xv
Figure 3.21: Gravimetric mass vs. reconstructed mass, Mascot 1997 ...... 91 Figure 3.22: ln[Io/1] of LIPM vs. ln[Ro/R] of reflectometer, of ambient aerosol (Teflon) ...... 93 Figure 3.23: ln[Io/1] of LIPM vs. ln[Ro/R] of reflectometer, of ambient aerosol (Nuclepore) ...... 93 Figure 4.1: Simple schematic diagram of the algorithm used in the system ...... 109 Figure 4.2: Growth factorf(RH) plotted against relative humidity (RH) ...... 115 Figure 4.3: Mean real refractive index of ambient aerosol vs. relative humidity ...... 115 Figure 4.4: Mean aerosol particle density, p (g cm-3), calculated using molar fraction as a function of relative humidity ...... 116 Figure 4.5: Mean aerosol particle density, p (g cm-3), calculated using mass fraction as a function of relative humidity ...... 116
Figure 5.1: Simulated and retrieved aerosol size distribution dN/dlogr (cm-2), using constrained linear technique (Stratospheric) ...... 131
Figure 5.2: Simulated and retrieved aerosol size distribution dN/dlogr (cm-2), using constrained linear technique (monomodal) ...... 131
Figure 5.3: Simulated and retrieved aerosol size distribution dN/dlogr (cm-2), using constrained linear technique (bimodal 1) ...... 132
Figure 5.4: Simulated and retrieved aerosol size distribution dN/dlogr (cm-2), using constrained linear technique (bimodal 2) ...... 132
Figure 5.5: Simulated and retrieved aerosol size distribution dN/dlogr (cm-2), using analytic eigenfunction technique (stratospheric) ...... 134
Figure 5.6: Simulated and retrieved aerosol size distribution dN/dlogr (cm-2), using analytic eigenfunction technique (monomodal) ...... 134
Figure 5.7: Simulated and retrieved aerosol size distribution dN/dlogr (cm-2), using analytic eigenfunction technique (bimodal 1) ...... 135
Figure 5.8: Simulated and retrieved aerosol size distribution dN/dlogr (cm-2), using analytic eigenfunction technique (bimodal 2) ...... 135 Figure 5.9: As in Figure 5.8, with 3 extra wavelengths ...... 137 Figure 5.10: As in Figure 5.4, with 3 extra wavelengths ...... 137
Figure 5.11:(a) 'taer as a function of wavelength and (b) size distribution for 3/11/96, xvi
16/12/96, and 28/2/97, morning ...... 139 Figure 5.12: Same as 5.11, afternoon of 13/11/96, 14/11/96, and 26/2/97 ...... 140 Figure 5.13:(a) Same as 5.11, morning of20/l/97, 3/2/97, and 10/7/97 ...... 142 Figure 5.14: Same as 5.11, afternoon of 1/5/97, 3/8/97, and 24/8/97 ...... 143 Figure 5.15:(a) Same as 5.11, morning of 24/6/97, 22/8/97, and 3/9/97 ...... 144 Figure 5.16: Same as 5.11, afternoon of 26/4/97, 28/5/97, and 15/9/97 ...... 145 Figure 5.17: Same as 5.11, morning of 21/6/97, 17/7/97, and 10/8/97 ...... 146 Figure 5.18: Same as 5.11, afternoon of 12/4/97, 6/7/97, and 8/8/97 ...... 147 Figure 5.19: Same as 5.11, morning of 22/12/96, 18/7/97, and 14/8/97 ...... 148 Figure 5.20: Same as 5.11, morning of 18/11/96, 5/1/97, and 18/8/97 ...... 149 Figure 5.21 Same as 5.11, afternoon of 20/1/97, 3/2/97, and 6/4/97 ...... 150 Figure 5.22: Same as 5.11, afternoon of 4/1/97, and 12/6/97, ...... 152 Figure 5.23: Same as 5.11, afternoon of 20/6/97, and 2/7 /97 ...... 153 Figure 5.24: Junge coefficient, (v*), vs. aerosol optical thickness, morning ...... 155 Figure 5.25: Junge coefficient, (v*), vs. the aerosol optical thickness, afternoon ...... 155 Figure 5.26: (a) Ambient and dry size distribution on the morning February 28, 1997, vs. particle radius, r. (b) is the, volume weighted distribution, r3n(r) ...... 157 Figure 5.27: (a) Ambient and dry size distribution on the afternoon of December 19, 1996, vs. particle radius, r. (b) is the, volume weighted distribution, r3n(r) ...... 158 Figure 5.28: Ambient and dry size distribution on the morning of August 22, 1997, vs. particle radius, r. (b) is the, volume weighted distribution, r3n(r) ...... 159 Figure 6.1: The standard case of diffuse reflection and transmission by a plane parallel atmosphere ...... 164 Figure 6.2: Phase Function of aerosol particle for wavelength A=0.515µ.m, afternoon of 30/4/97 ...... 174 Figure 6.3: Atmospheric transmission function t(Ti,/lo), vs. cosine of the solar zenith, afternoon of 30/4/97 ...... 175 Figure 6.4 (a): Calculated diffuse-direct ratio for 30 April, 1997 afternoon ...... 177
Figure 6.5: Measured diffuse to direct ratio, meas, for the morning and afternoon, 30/4/97 ...... 179 Figure 6.6: Measured and computed diffuse to direct ratio as a function of solar zenith, xvii
330/4/97 ...... ········· ············ ... ·································· ...... 184
Figure 6.7: Diffuse to direct ratio,
Figure 8.8: Taer(eigenvalue) vs. Taer(quadratic) at A= 0.934µm for the afternoon ...... 218 Figure 8.9: Modified Langley plot for the morning of April 29 1997 ...... 219
Figure 8.10: MFRSR calibration constant, V0 , from October 1996 to September 1997. 219
Figure 8.11: Water vapour column retrieval, u (gm cm-2), on June 10 1997 ...... 221
Figure 8.12: Water vapour column retrieval, u (gm cm-2), on August 1 1997 ...... 221
Figure 8.13: Water vapour column retrieval, u(gm cm-2), on February 19 1997 ...... 222
Figure 8.14: Water vapour column retrieval, u (gm cm-2), on April 29 1997 ...... 222
Figure 8.15: Temporal variation of the averaged water vapour column, u (gm cm-2), as observed by the MFRSR, September 25 1996, to September 16 1997 ...... 223 xix
List of Tables
Table 2.1: MFRSR spectral bands location, bandwidth, and main trace species measured ...... 9
Table 2.2: Seasonal mean of aerosol optical thickness, 't'aer, and their standard deviation 27
Table 2.3: Summary of the calculated effective scale height Heff (km), using the two different parameters ...... 43 Table 3.1: Refractive index and density for different species ...... 79 Table 3.2: Calculated refractive index and density of particle carbon types ...... 81 Table 3.3: Summary of measured and calculated mass absorption coefficient, for the different types of black carbon ...... 84 Table 3.4: Refractive indices and particles density of various aerosol components at wavelength A.=0.650 µm ...... 90 Table 4.1: Equilibrium relations and constants ...... 101 Table 4.2: Pitzer method binary activity coefficients ...... 103 Table 4.3: Relative humidity of deliquescence ...... 106 Table 4.4: Aerosol chemical composition as a function of the ambient relative humidity for the sulfate deficient case ...... 107 Table 4.5: Aerosol chemical composition as a function of the ambient relative humidity for the sulfate rich case ...... 108 Table 4.6: Average particle mass and fitting parameters used in Equation (4.29), ...... 113 Table 4. 7: Density and refractive index of the main aerosol particle species ...... 117 Table 5.1: Summary of the 4 simulated aerosol lognormal size distribution ...... 129
Table 6.1: Summary of the obtained complex refractive index K, and reflectivity A . .... 181 Table 7.1: Wavelengths and refractive indices for MFRSR set of wavelength, for different aerosol types ...... 192 Table 7.2: Coefficients used for prediction of 0.61 µm measurements MFRSR wavelengths ...... 194
Table 7.3: Ozone absorption coefficient, a1 1/(atm cm), ...... 197 XX
Table 8.1: Coefficients used for prediction of 0.934 µm measurements for MFRSR wavelengths ...... 209 Table 8.2: Coefficients k and b obtained for the MFRSR using LOWTRAN 7 and MODTRAN 3 ...... 216 Chapter 1
Introduction
1.1 Overview
Aerosol particles can either increase or decrease the incoming radiation by scattering and absorption (direct effect), and by changing the reflectivity, duration, and extent of clouds (indirect effect). Aerosols are small particles or droplets in the atmosphere, with sizes on the order of a micrometre. Their effects on atmospheric radiation are a leading source of uncertainty in predicting the anthropogenic contribution to global radiative forcing of climate. Much of this uncertainty is due to lack of knowledge of aerosol optical and physical properties, as well as their geographical and seasonal distribution. Modelling efforts by Charlson et al. (1992) concluded that for better quantification of aerosol radiative forcing and of its relation to anthropogenic emission, it was essential to have continuous global monitoring of aerosol using space-borne instruments. It is also necessary to connect this global data set to concurrent ground-based measurements of optical properties, and to in situ surface and aircraft measurements of chemical and microphysical properties. In the Sydney area there have been very few studies of the aerosol particle properties in atmospheric column using remote sensing techniques. Most of the previous studies of aerosol involved in situ measurements (ERDC, 1995; Ayres, et al., 1999a) which measure the aerosol ingested into an instrument system. Our research here involves the use of ground-based remote sensing measurements of the total aerosol column, with in situ measurements, to study the aerosol optical, physical, and chemical properties. Only brief overview of the problem is given here. More detailed overview and literature background will be given at the beginning of each chapter. Chapter 1 Introduction 2
1.2 Objectives
The aim of this thesis is to improve and validate methods of measuring aerosol properties by means of ground-based remote sensing and in situ instruments, as well as modifying models of inferring column abundances of the radiatively active gases, ozone and water vapour. The two methods are combined to extract maximum information about key aerosol particle parameters, which will lead to a better understanding of aerosol optical and physical properties. This will help build up a comprehensive database of the seasonal and inter annual variations of aerosols, ozone and water vapour column in Sydney. It will also reduce uncertainties in effects of anthropogenic aerosols on climate, and lead to improvements in modelling efforts to determine their direct radiative impacts on climate. Continuous observations were carried out, using a Multifilter Rotating Shadowband Radiometer (MFRSR), where simultaneous measurements of global, diffuse and direct components of solar irradiance were collected daily at one broadband, and six narrowband channels at A= 415.4, 501.7, 615.5, 672.7, 869.8, 933.6 nm. Information about aerosol optical thickness was obtained by removing the molecular and gaseous contributions, and the total column size distribution of aerosol particles was calculated using constrained linear inversion (King et al., 1978). The size distribution is used to calculate total column mass of aerosols. Continuous in situ measurements of the aerosol scattering coefficient (nephelometer), particle mass concentration (PMlO TEOM), and relative humidity were obtained from the NSW EPA Randwick Air Pollution station (less than 1000 m away from the MFRSR location). 24h fine particle samples were collected using PM2.5 ASP sampler by ANSTO, at Mascot (less than 5 km away from our site), every Wednesday and Sunday. Elemental composition of aerosol samples was determined using Accelerator Ion Beam Analysis (IBA), while absorption coefficient was measured using the Laser Integrating Plate Method (LIPM). Diurnal and Temporal variations of the aerosol optical thickness, scattering coefficient, and mass concentration were analysed to study the long-term variability of aerosol particles. Information of aerosol optical thickness and total mass column, obtained from the MFRSR column measurements were compared to the aerosol Chapter 1 Introduction 3 scattering coefficient, and mass concentration surface measurements. However, because the inter-comparison of the in situ dry measurements was made with remote retrievals, i.e. under ambient conditions, a correction for relative humidity growth is in order. This has been accomplished by using the elemental composition of the aerosol as an input to the thermodynamic model SCAPE2, to calculate the particle water uptake, and estimate an enhancement growth factor. Both molar fraction and mass fraction approaches were used to calculate refractive index and density of particles. Empirical models were used to determine values of these parameters as a function of relative humidity. The ambient particle density was required for total mass column determination. Determining single scattering albedo was essential to estimate scattering optical thickness, to be compared with scattering coefficient. This was calculated using index of absorption, obtained using diffuse to direct ratio (King and Herman, 1979). An intensive study to test and validate the Laser Integrating Plate Method (LIPM) of determining absorption coefficient, and black carbon mass was carried out. Measurements of LIPM were correlated with reflectometer measurements and IBA chemical composition analysis. Results show that the value of specific mass absorption coefficient, or efficiency E = 10 m2t 1 previously used for black carbon mass determination, and widely accepted for diesel fuel emission, is an inappropriate choice.
More accurate estimates of E =7 and 6 m2t 1 for soot and ambient aerosol particles were experimentally determined. The new values were in good agreement with the theoretically determined values, using Mie calculation. Refractive index and density, needed for Mie calculations, were determined using the aerosol chemical composition, using mass fraction approach. Eigenvalue analysis is used for inferring total ozone column and aerosol optical thickness from spectral measurements. Inferred ozone using the new method is compared with the values obtained using the method proposed by (King and Byrne, 1976), and with satellite ozone measurements. Results of inferred column ozone showed an excellent agreement with satellite ozone measurements, obtained by ADEOS and EP/TOMS instruments. The mean percent difference with TOMS measurements was 1 % and 2 %. Accurate ozone column estimation will improve the accuracy of aerosol optical thickness and size distribution retrieval. Eigenvalue analysis was also applied to the problem of Chapter 1 Introduction 4 determining water vapour column, using radiometer measurements.
1.3 Thesis outline
This dissertation is constructed in the following manner: Chapter 2 gives a description of the instrumentation used in this work, and the method used to calculate aerosol optical thickness. Results of the diurnal and temporal variations of aerosol optical thickness are presented. Column measurements of total mass column and scattering optical thickness are compared to surface mass concentration and scattering coefficient measurements. Both sets of measurements were combined to determine a value of effective scale height, which will improve our knowledge of the aerosol vertical structure. Chapter 3 is about the experimental measurements of the absorption coefficient and black carbon mass determination of soot and ambient aerosol samples, using the Laser Integrating Plate Method {LIPM) technique. The use of Accelerator Ion Beam Analysis (IBA) to determine the chemical composition of particle samples is investigated. Measurements of LIPM are compared against the reflectometer measurements, and against theoretical prediction. It showed that the value of mass absorption coefficient E = 10 m2i 1 previously used for black carbon mass determination, is an inappropriate choice. In Chapter 4 the elemental composition of particles, determined using IBA analysis, was used as an input to the equilibrium thermodynamic model of inorganic particles SCAPE2, to study the hygroscopic growth of particles. Molar fraction and mass fraction approach were used to calculate real refractive index and density of particles. Empirical models are introduced to calculate these key parameters as a function of relative humidity for the aerosol particle observed in Sydney. In Chapter 5 the use of the constrained linear inversion technique and analytic eigenfunction theory to obtain the aerosol size distribution were investigated, using both synthetic data and real measurements of MFRSR. The results obtained were used to study the seasonal behaviour of aerosol particles in Sydney, and the effect of relative humidity on particle size distribution. In Chapter 6 the diffuse to direct ratio of MFRSR measurements was used to determine the complex refractive index of particles and ground reflectivity. This work involves the use of a radiative transfer model for theoretical prediction of the ratio. While Chapter 1 Introduction 5 results of the ground albedo were unrealistic in many cases, values of index of absorption, and single scattering albedo were estimated, and were consistent with the chemical composition analysis in chapter 3, which showed that black carbon comprises up to 30% of the total mass of aerosol particles. In Chapter 7 we introduce a new method for inferring total ozone column from radiometer measurements. Eigenvalue analysis is used to predict aerosol optical thickness at the Chappuis band, centred at 0.6lµm, from measurements at other wavelengths. Results are presented and compared against King and Byrne method, and against ADEOS and EP/TOMS satellite instruments. The new method gave an excellent agreement of 1 % and 2 % respectively, with satellite measurements. Chapter 8 is about the water vapour column retrieval from the MFRSR solar transmittance measurements in the 0.934 µm band, using the Modified Langley technique. The atmospheric models LOWTRAN 7 and MODTRAN 3 were used to determine the instrument coefficients required for water vapour retrieval. We also propose the use of eigenvalue analysis to predict aerosol optical thickness at the water vapour channel. Results are presented to study the diurnal and temporal variations of water vapour column. Chapter 9 briefly outlines the main findings and conclusions of this work, as well as the proposed recommendations and future work. Chapter 2
Aerosol Optical Thickness Inferred From Spectral Radiometer Measurements
2.1 Introduction
The study of the optical and physical properties of aerosols is important mainly because of their significant impact on climate forcing. They have a direct effect by scattering and absorbing incoming solar radiation, and an indirect effect through their influence on cloud formation. They also play a considerable role in the formation and transport of chemical species. Locally they can affect the visual air quality as well as affecting human health. Because of its short lifetime, of almost a week, the concentration of the tropospheric aerosol varies with time and location, and its spatial distribution is highly inhomogeneous and strongly correlated with its sources. One of the most important parameters is the aerosol optical depth or thickness Taer· It is commonly used in remote sensing studies, and gives a measure of the attenuation of incoming sunlight, due to both scattering and absorption, as the light travels through the atmosphere. The extent of attenuation depends on the wavelength of the scattered light and the size of particles causing this scattering. These optical thickness measurements can then be inverted to determine the size distribution of the particles. Another important parameter, which is widely used in visibility studies, is the scattering coefficient, /3sp, which is measured using an integrating nephelometer. Assuming horizontal homogeneity, the relation between the vertical optical thickness and the extinction coefficient can be expressed, at a given wavelength, as the integral over height to the top of the atmosphere, of the aerosol extinction coefficient f3ext,p.
~
Taer = J/3ex1,/A,z)dz 0
The aerosol extinction coefficient is given by Chapter 2 Aerosol Optical Thickness Measurements ... 7
f3ext,p = /Jap + /3sp, where /3ap is the absorption coefficient, and /3sp is scattering coefficient. In this chapter, we present measurements of the variation of aerosol optical thickness over Sydney, using a Multifilter Rotating Shadowband Radiometer (MFRSR). These results are used to provide more information about the long-term variability of spectrally resolved aerosol optical thickness, as well as the diurnal variability of aerosol. Aerosol column measurements of the MFRSR will be compared to the in situ measurements of the nephelometer and TEOM PMlO. An average value of an effective scale height is estimated from the two sets of measurements, which will improve our knowledge of the aerosol vertical structure.
2.2 Instrumentation
2.2.1 Multifilter Rotating Shadowband Radiometer (MFRSR) A Multifilter Rotating Shadowband Radiometer (MFRSR) instrument was installed at the Kensington campus of the University of New South Wales, Sydney, located at 33°55'11.6"s and 151°13°40.i'E, and is 85 m above sea level. Aerosol optical thickness measurements were taken starting from December 12 1995. On June 10 1996, there was a problem with the motor that controls the band's movement, and the water vapour channel measurements. The instrument had to be sent to the manufacturer, where it was fixed and re-calibrated. Measurements were resumed on September 25 1996. The instrument provides ground-based measurements that use interference filters and photodiode detectors. It takes measurements (in the spectral range of 400 nm to 950 nm) of total horizontal, diffuse horizontal, and calculates the direct normal spectral irradiance. Figure 2.1 and Figure 2.2 are a schematic diagram of the basic geometry of the MFRSR, and a photo of the MFRSR. An automated shadow band, controlled by a microprocessor, is used to alternately shade the diffuser. It can block a strip of sky with 3.3° umbral angle. Chapter 2 Aerosol Optical Thickness Measurements ... 8
______Two additional ---;;:--- measurements are made to / I /// >- compenaa!e tOf exteat aky The bloci Stepper motor • 8 Figure 2.1: Schematic diagram of the Multi fi lter Rotating Shadowband Radiometer, MFRSR, and its geometry. Figure 2.2: Photo of the MFRSR. Chapter 2 Aerosol Optical Thickness Measurements ... 9 Table 2.1: MFRSR spectral bands location, bandwidth, and main trace species measured. Channel Wavelength, A(nm) Bandwidth Trace Species (nm) 1 Broadband 2 415.4 9.7 aerosol 3 501.7 10.1 aerosol and ozone 4 615.5 10.0 ozone and aerosol 5 672.7 10.0 aerosol and ozone 6 869.8 10.0 aerosol 7 933.6 11.2 water vapour Chapter 2 Aerosol Optical Thickness Measurements ... JO The first measurement is taken while the Shadowband is at nadir, or home position, to give total horizontal irradiance. Then the band starts rotating and makes 3 more measurements. The first one is made with the band rotated 9° to the first side of the sun, the second position, for the diffuse horizontal irradiance, blocks the sun completely, and the last one is made with the band at 9° to the other side of the sun. The side measurements enable the system to correct for the excess sky blocked during the sun-block measurements (MFR-7, 1995; Harrison et al., 1994a). The direct normal component can then be calculated from the total horizontal and diffuse usmg Etotal-horiz = £diffuse + COS ( 0) X Edirect-nonnal (2.1) where Bis the solar zenith angle. The entire sequence is completed m less than 15 seconds, which enables the instrument to obtain 4 readings per minute. The Instrument makes 7 spectrally resolved measurements: one broadband (300-1100 nm), and six narrowband channels, at A= 415.4, 501.7, 615.5, 672.7, 869.8, 933.6 nm. Table 2.1 is a summary of the MFRSR spectral bands location, bandwidth, and main trace species measured by each of the instrument channels. Figure 2.3 is the instrument's filters spectral response, superimposed to the atmospheric transmittance using LOWTRAN 7 (Kniezys et al., 1988), midlattitude summer, at 0° zenith angle. 2.2.1.1 Angular corrections Measurements of global or total irradiance are usually made with the assumption that the instrument response is Lambertian, or directly proportional to the cosine of the incident angle. However, this is not exactly true, as there is no instrument that is perfect in this regard. The host software of the instrument automatically performs angular corrections of the direct-normal irradiance for Lambertian receiver error using (Harrison et al., 1994a) C( if, 0) = 90 - if, 1 + if, 1 (2.2) ' 90 foorth {0) 90 hast {0) angle. angle. zenith O" O" 1000 1000 at at , , Summer 900 900 Midlatitude Midlatitude , , 7 800 800 LOWTRAN LOWTRAN A.(nm) A.(nm) from from 700 700 transmittance transmittance Wavelength, Wavelength, 600 600 atmospheric atmospheric the the and and 500 500 ofMFRSR ofMFRSR response response 400 400 spectral spectral 0 0 1 1 0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 o.5 o.5 0.6 0.6 0.7 0.7 0.8 0.8 0.9 0.9 Filters Filters : : ~ ~ u, u, ro ro ~ ~ C: C: ~ ~ ~ ~ ·e ·e 2.3 Figure Figure Chapter 2 Aerosol Optical Thickness Measurements ... 12 Where C is a multiplying correction factor, 0 is the solar zenith angle, r/J is the solar azimuth angle in degrees, and !north and feast are the normalised angular response functions in Figure 2.4 and Figure 2.5. 2.2.1.2 Langley analysis Channels 2 to 6 are used to measure the aerosol optical depth according to Bouguer Lambert-Beer law of attenuation, I - I R-2 --r.m " - o,. e (2.3) Where I;,. is the direct-normal irradiance calculated using Equation (2.1 ), / OA is the extraterrestrial intensity ( r = 0), R is the Earth-Sun distance, in astronomical units, rA is the atmospheric optical thickness, or optical depth at a specific wavelength, and m is the relative path length or relative air mass factor, where m = sec( 0). If 0 ~ 80, a special correction will be needed for the Earth curvature effects (Harrison et al., 1994b). The total optical thickness can be obtained using the well-known Langley technique. Equation (2.3) can be rewritten in logarithmic form as 2 In(/,.) = ln(/0,. R- ) - r,. m (2.4) A plot of /n(/,.)against m will produce a straight line, under ideal conditions of a fairly cloudless clear day and stable atmosphere (usually intervals of m between 2-6 for either morning or afternoon periods). 2 The slope, -rA, gives the optical thickness and the intercept ln(/0 ,. R- ) gives the instrument calibration constant I OA for a particular wavelength, when corrected for the Earth-Sun distance, R. Figure 2.6 is a plot of the observed solar irradiance components of global horizontal, diffuse horizontal and direct normal in W/m2/nm, at A= 502 nm, as measured on March 19 1997. Figure 2.7 is Langley regression plot for the 5 narrow band direct normal irradiance, for the morning of March 19 1997. The atmospheric total optical thickness is the sum of optical thicknesses related to the different atmospheric components. It can be expressed as T=TR +'rgas +raer (2.5) Chapter 2 Aerosol Optical Thickness Measurements ... 13 Q) 1/) C --Broadband 0 0. --415.4nm 1/) Q) a:: 501 .7nm --615.Snm -0 Q) .!Q --672.7nm ro --869.Bnm E ... --933.6nm z0 -90 -60 -30 0 30 60 90 Angle ((degree), from South to North Figure 2.4: Angular response error of MFRSR angle from south to north. Q) 1/) C --Broadband 0 0. 415.4nm 1/) Q) --501 .7nm a:: --615.Snm -0 Q) --672.7nm .!Q ro --869.Bnm E... f --933.6nm z0 ~ -90 -60 -30 0 30 60 90 Angle ((degree), from West to East Figure 2.5: Angular response error ofMFRSR angle from west to east. Chapter 2 Aerosol Optical Thickness Measurements ... 14 1.6 1.4 Direct Normal --~-\, .,,,. I 1.2 I Global Horizontal (]) C.) 1 C rn 0.8 "C t...rn t... 0.6 0.4 Diffuse Horizontal 0.2 0 20:00 23:00 2:00 5:00 8:00 Time (UTC) Figure 2.6: Plot the observed irradiance components of global horizontal, diffuse horizontal and direct normal, W/m 2/nm, for the March 19 1997. 0.4 0 -0.4 - -0.8 +415.4 nm -C x 501.7 nm -1.2 -615.5nm 672.7 nm -1.6 ,i:: 869.8 nm -2 2 3 4 5 6 Air mass, m Figure 2. 7: Langley regression plot for the 5 narrow-band direct-normal solar irradiance, in W/m2/nm, for the morning of March 19 1997. Chapter 2 Aerosol Optical Thickness Measurements ... 15 Where 'tR is the molecular (Rayleigh) scattering optical thickness, 'tgas is the gaseous absorption optical thickness, and Taer is the aerosol extinction optical thickness. For channels 2 and 6, Tgas is assumed to be zero. Channels 3, 4 and 5 are assumed to be only contaminated by ozone (Chappuis band). The previous equation is rearranged as 7 aer = T-TR -To3 (2.6) Where TR is Rayleigh optical thickness and is given by (Robinson, 1966; Paltridge and Plat, 1976) T, = (;:) x 0.00888X'" (2.7) ps is the atmospheric pressure at the site, p 0 = 1013.25 hPa is the standard sea level pressure, and To3 is the ozone optical thickness. Determination of the total ozone column is discussed in detail in Chapter 8. 2.2.1.3 Junge coefficient The Junge coefficient is calculated from the wavelength dependent aerosol optical thickness using the relationship proposed by Angstrom (1929) T aer = /JA-a (2.8) Where a is the wavelength exponent, which is related to the size distribution, while /3 is the turbidity coefficient, or the aerosol optical thickness that corresponds to the 1 µm wavelength. Both a and fJ are independent of wavelength. The Junge coefficient, v*, is related to Angstrom exponent by v· =a+2 (2.9) 2.2.2 PTBl00A analogue barometer PTB 1OOA analogue barometer features the Barocap silicon capacitive absolute pressure sensor, which was developed by Vaisala. It has been designed to provide accurate, stable and continuous measurements of the barometric pressure. The output signal is O to 5 VDC. The barometric pressure (P) can be calculated from the measured output voltage (U) using Chapter 2 Aerosol Optical Thickness Measurements ... 16 26 P = 800hPa + 0hPa x U{V) (2.10) 5V The barometer was connected to the MFRSR's data logger, and the calibration file of the instrument was modified to calculate the atmospheric pressure directly, using the above equation. 2.2.3 Surface measurements Measurements of the aerosol scattering coefficient, fine particle mass and meteorological parameters were obtained from the NSW EPA Randwick Air Pollution station (less than 1 km away from the MFRSR location). 2.2.3.1 Belfort Model 1590 Nephelometer Continuous measurements of light scattering /3sp were made using a Belfort Model 1590 nephelometer, with an effective response centred at 'A = 530 nm. Light scattering, corrected for Rayleigh scattering, is sampled every 2 minutes. The hourly average measurements were used for this study. An air preheater is used in the sample line to minimise the hygroscopic effects. Increasing the air temperature by 8 - 9° C reduces the relative humidity of the sample air below 60 % (Ruby et al., 1991). However, Australian Standard says that the nephelometer outlet temp shall be maintained at 15 - 25° C above ambient. 2.2.3.2 PM10 Tapered Element Oscillating Microbalance (TEOM) Aerosol particle PMlO mass concentration measurements were collected using the Tapered Element Oscillating Microbalance (TEOM). The sensor uses a cyclone size selective inlet to remove particles greater than 10 µm in aerodynamic diameter, where the sample stream and sensor are heated to 50° C to dry the collected aerosol particles. To convert aerodynamic into geometric diameter, we simply divide by square root of particle density, p (g cm-3). 2.3 Results and discussion The MFRSR calibration is based on estimating 10J, the instrument calibration constant, obtained from the successful Langley plots. Figures (2.8, 9, 10, 11 and 12) show the long-term calibration history of the instrument, just after the first Chapter 2 Aerosol Optical Thickness Measurements ... 17 3 2.5 2 -0 1.5 1 0.5 0 Sep Nov Jan Mar May Jui Aug Oct Month 2 Figure 2.8: Time series of the MFRSR calibration constant, 10 (W/m /nm), at A= 415.5 nm. The points are the monthly average 10 , while the dashed line is line ofbest fit. Average 10 = 2.159, with a standard deviation of CJ= 0.149. 2 1.5 0 - 1 0.5 0 ---l-....J....---,...--1..----!,...... _+---'---+------+- ...... ---+----'~ Sep Nov Jan Mar May Jui Aug Oct Month 2 Figure 2.9: Time series of the MFRSR calibration constant, 10 (W/m /nm), at A= 501.7 nm. The points 2 are the monthly average 10 , while the dashed line is line of best fit. Average 10 = 1.844 (W/m /nm), with a standard deviation of CJ= 0.112. Chapter 2 Aerosol Optical Thickness Measurements ... 18 1.5 0.5 0 Sep Nov Jan Mar May Jui Aug Oct Month 2 Figure 2.10: Time series of the MFRSR calibration constant, /0 (W/m /nm), at A= 615.5 nm. The points 2 are the monthly average / 0 , while the dashed line is line of best fit. Average /0 = 1.636 (W/m /nm), with a standard deviation of cr = 0.079. 1.6 --.------...... 1.4 1.2 1 ~ 0.8 0.6 0.4 0.2 0 +--'--+---'---+---'-+--.._-+---'--+---'---+---'----I Sep Nov Jan Mar May Jui Aug Oct Month 2 Figure 2.11: Time series of the MFRSR calibration constant, /0 (W/m /nm), at A= 672.7 nm. The points 2 are the monthly average / 0 , while the dashed line is line of best fit. Average /0 = 1.0 (W/m /nm), with a standard deviation of cr = 0.225. Chapter 2 Aerosol Optical Thickness Measurements ... 19 1.2 --r------1 0.8 ~0.6 0.4 0.2 0 +-...... -+------+------,,--.__-1-....__-+--'---+------1 Sep Nov Jan Mar May Jui Aug Oct Month 2 Figure 2.12: Time series of the MFRSR calibration constant,/0 (W/m /nm), at A= 869.7 nm. The points 2 are the monthly average 10 , while the dashed line is line of best fit. Average 10 = 0.937 (W/m /nm), with a standard deviation of er= 0.036. Chapter 2 Aerosol Optical Thickness Measurements ... 20 calibration of the instrument, from Sept. 1996 to Sept. 1997. The plots shows the obtained calibration constant, / 0;;,_, time series for each channel. The points are the monthly average of /0;;,_, and the dashed line represents the best fit of these averages. It shows a reasonably stable / 0;;,, for all but the 673 nm channel. The standard deviation of the calibration constant monthly average was cr = 3-4% of calibration constant /0;;,,, for channel 2, 3, and 4, and 2% for channel 6. The first 3 channels showed a small drift of calibration with time, starting from May 1997. Calibration constant for channel 5, )... = 672.7, drifted significantly with time up to 50 % of the value determined during the first month of operation, Figure 2.11. This drift in the calibration constant value of channel 5 is expected to affect only the flux measurement, /(673), but does not affect the optical thickness values, derived from this channel. The advantage of using Langley technique for aerosol optical thickness retrieval is that it does not involved the use of the calibration constant of the instrument, since this value is a relative, not an absolute measurement. It seems that this problem that affected channel 5 has been experienced by many of the MFRSR users. The Atmospheric Radiation Measurement (ARM) program, which runs more than 25 MFRSR instruments, deployed on the Southern Great Plains (SGP), reported in their homepage site (http://www.arm.gov/docs/instrument/ static/mfrsr.html) that as of 11 March 1998, the calibration of the MFRSR was problematic. In some channels, the calibration showed a significant drift with time, in some cases the error in the spectral fluxes might be as much as 50 %. Nonetheless, they recommended the MFRSR users of flux data to be aware of the problem, which does not affect any of the optical depths derived from the MFRSR. 2.3.1 Temporal variation of aerosol optical thickness Figure 2.13 and Figure 2.14 illustrate the day-to-day variability of aerosol optical thickness, Taer (A), measured in Sydney from December 16 1995 to September 16 1997, for the 5 different wavelengths, during both the morning and the afternoon, respectively. One can easily notice the gap from June 10 to September 25 1996,caused by the instrument malfunction. Only days of good / 0;;,,, or reasonable cloudless periods were included here The significant variability of aerosol is mainly due to changes in local aerosol sources and meteorology, as no major event took place during measurements. ::::, C) 0) ...... <( , 1997 ::::, C 0) ...... -, 16 Sept. to c. ... ...... <( 0) 1995 16 Dec. Q) from u. .J::J. 0) ...... . MFRSR the wavelengths 1rl by et> 0) 0 different 5 measured Month the as , u ~ 0 for t..,.(A) , period, C) ::::, thickness et> <( 0) morning the optical during C ::::, aerosol et> 0) -, of variation c. ... et> 0) <( Temporal : 870 502 616 673 416 13 . 2 Q) et> u. 0) .J::J. ~ --*- ------+- Figure (.) Q) 0) LO 0 l 0 0.1 0.2 0.3 0.05 0.15 0.25 Q) vi C e (.) "' 0 "' Q) 0 ~ <( ~ £ ~ a J 7, 7, 199 16 16 Sept. Sept. to to 1995 1995 16 16 . . Dec from from . . MFRSR MFRSR the the wavelengths by by different different 5 5 Month Month measured measured the the as as , , for for , , 'taer(A) , , thickness afternoon period afternoon the the optical optical during during aerosol aerosol of of variation variation Temporal Temporal : 870 870 673 673 616 616 502 502 416 416 2.14 -&- ---- - -+- ""*- Figure Figure I I 0 0 15 15 05 05 35 35 . . 0.1 0.1 0.2 0.2 . 0.4 0.4 03t 03t 0 0 0.25 0.25 0 Q) Q) (/) (/) t!1 t!1 e e ~ ~ C: C: {.) {.) ~ ~ §- 0 0 - ~ ~ .!,it. .!,it. :c :c ~ ~ Chapter 2 Aerosol Optical Thickness Measurements ... 23 ...-...... c..o 0.2 " CJ) I- CJ) 0.14 a., C I- ~ 0.12 0 0.1 I- ..c "' -C'IJ 0.08 0 :;::::; 0.. 0.06 0 0 0.04 ~ CJ) 0 L.. 0.02 a., J I I <{ 0 - +- I I I I I I I I I _. (.) C .0 ... >, C :::::l CJ) 0.. (.) > Q) 0.. co :::::l :::::l Q) 0 Q) co <( -, -, LL ~ -, <( C/) 0 z 0 Month Figure 2. I 5: Monthly mean value of the aerosol optical thickness, 'ac,O-), using all values measured by the MFRSR from Dec. I 6 I 995 to Sept. I 6 I 997, during the morning and the afternoon periods at A= 4 I 6 nm. The vertical bars give the standard deviation of the monthly means. ...-.. 0.14 0 ~- 0 - DAM LO _,_ '-C' 0.12 Q) p"' •PM 0.1 - 1- CJ) CJ) a., - C 0.08 ~ .2 ..c- 0.06 - mu :;::::; 0.04 - 0 0.. 0 - 0 0.02 CJ) e I I I I I ' ~ a., 0 I I I I I I I I I I _. <{ .__ >, C CJ) 0.. (.) C .0 :::::l (.) > co Q) 0.. co :::::l -, :::::l Q) 0 Q) -, LL <( ~ -, <( C/) 0 z 0 Month Figure 2. I 6: Monthly mean value of the aerosol optical thickness, ' ac,(A), using all values measured by the MFRSR from Dec. I 6 I 995 to Sept. I 6 I 997, during the morning and the afternoon periods at A= 502 nm. The vertical bars give the standard deviation of the monthly means. Chapter 2 Aerosol Optical Thickness Measurements ... 24 m.... 0.12 Figure 2.17: Monthly mean value of the aerosol optical thickness, 't,cr(A), using all values measured by the MFRSR from Dec. 16 1995 to Sept. 16 1997, during the morning and the afternoon periods at A = 6 I 6 nm. The vertical bars give the standard deviation of the monthly means . ..--.. C'? 0.1 I'-- Figure 2.18: Monthly mean value of the aerosol optical thickness, 'tac,(A), using all values measured by the MFRSR from Dec. I 6 I 995 to Sept. 16 I 997, during the morning and the afternoon periods at A= 673 nm. The vertical bars give the standard deviation of the monthly means. Chapter 2 Aerosol Optical Thickness Measurements ... 25 0 0.09 I'-- co '-C 0.08 -- DAM Q) ~"' 0.07 - •PM ui Cl) - Q) 0.06 C ~ - .!::? 0.05 ..c - 0.04 - Ctlu :;::; 0.03 - 0.. 0 0.02 0 Cl) ,._0 0.01 Q) I I I <( 0 +- +- I I ,._ >, C ..... (.) C .0 ::J 0) 0. (.) > co Q) 0. co ::J ...., ::J Q) 0 Q) ...., LL Figure 2. 19: Monthly mean value o f the aerosol optical thickness, 'tac,(A), using a ll values measured by the MFRSR from Dec. 16 1995 to Sept. 16 1997, during the morn ing and the a fternoon periods at A = 870 nm. The vertical bars give the standard deviation of the monthly means. Chapter 2 Aerosol Optical Thickness Measurements ... 26 The monthly mean optical thickness of aerosol particles and their standard deviation of all 5 wavelengths, for the morning and the afternoon averages, based on all the available observations, are presented in Figure 2.15, 16, 17, 18, and 19. As it can be seen from the Figures, the highest mean optical thicknesses for both the morning and the afternoon periods occurred on January. To investigate the seasonal variation of the aerosol optical thickness, seasonal averages were calculated of the daily measurements, at 5 different wavelengths for the morning and the afternoon periods. Table 2.2 is a summary of these seasonal averages, and their standard deviations. For the morning mean values, spring (Sept.-Nov.) showed the lowest mean of aerosol optical thickness, while none of the other seasons showed a clear maximum. In the afternoon, summer (Dec.-Feb.) was the period of the highest mean, and again, no season showed clear minima of mean values. On average, summer had the highest mean value of aerosol optical thickness. Although the measurements presented cover almost a period of two years, more measurements are required in order to obtain a more conclusive seasonal pattern of the aerosol optical thickness. 2.3.2 Temporal variation of Junge coefficient The temporal variation of Junge coefficient, v*, which is related to the size of aerosol particles, was calculated for the period of September 25 1996 to September 16 1997. Figure 2.20 and Figure 2.21 are plots of time series of the calculated v*, for the morning and afternoon respectively. The points are the monthly mean value, and the line is the best fit of the monthly means. Values of v* ranged from 0.56 to 5, with an average value of 2.75, and standard deviation of 0.67 for the morning, while the afternoon average was 2.8 with standard deviation of 0.8. No seasonal pattern was observed for v* values, which means that changes of the aerosol size were not seasonal. More detailed work on aerosol size distributions, obtained through inversion methods, will be discussed in Chapter 5. 2.3.3 Diurnal variation of aerosol optical thickness In order to study the significant variability of the optical thickness during the day, instantaneous optical depth values can be obtained, to describe the minute-by-minute behaviour of Taer, by combining Equations (2.4) and (2.6) Chapter 2 Aerosol Optical Thickness Measurements ... 27 Table 2.2: Seasonal mean of aerosol optical thickness, 'taen and standard deviation of each season of the year, at 5 different wavelength for the morning and the afternoon periods. Morning Afternoon Wavelength, A Taer a Taer 416 Winter 0.0616 0.0411 0.0715 0.0379 Spring 0.0471 0.0465 0.0760 0.0765 Summer 0.0657 0.0522 0.0833 0.0529 Autumn 0.0719 0.0425 0.0652 0.0518 512 Winter 0.0589 0.0314 0.0626 0.0309 Spring 0.0461 0.0375 0.0599 0.0444 Summer 0.0610 0.0412 0.0696 0.0392 Autumn 0.0618 0.0358 0.0571 0.0404 616 Winter 0.0473 0.0234 0.0482 0.0236 Spring 0.0372 0.0289 0.0442 0.0274 Summer 0.0513 0.0313 0.0567 0.0285 Autumn 0.0511 0.0289 0.0463 0.0287 673 Winter 0.0423 0.0213 0.0429 0.0210 Spring 0.0331 0.0260 0.0391 0.0237 Summer 0.0479 0.0284 0.0534 0.0258 Autumn 0.0472 0.0271 0.0422 0.0252 870 Winter 0.0325 0.0208 0.0335 0.0157 Spring 0.0229 0.0205 0.0310 0.0192 Summer 0.0429 0.0243 0.0469 0.0234 Autumn 0.0391 0.0259 0.0347 0.0203 Chapter 2 Aerosol Optical Thickness Measurements ... 28 6 ' T I ' ' -V* ...... Monthly -~ 5 '- <( I _. i! -C 4 ~ 11 i Q) I ·o 1111 I~I :E 3 i"-- I '~ :~ - Q) I I l ... ~ I lilt" 0 """- I .,..-1.-- ~ () :: 2 ~ f K' I - Q) ii I I C) 11 --- C II Ill I ::::, 1 - I -, 11 I I 11 I I 0 11 ' ! 1ll1 I l:1 J a. (.) C .0 ...... C Q) 13 Q) ro Q) ro a. ::::, en 0 0 -, lL ~ <( -, Month Figure 2.20: Time series of the calculated Junge coefficient, v', at the morning, from September 25 1996, to September 16 1997. The points are the monthly mean, and the line is the best fit of the monthly means. 6 -V* ~ 5 -.-Monthly -a.. _. -C 4 ·oQ) :E 3 Q) 0 () Q) 2 C) C -,::::, 1 0 _. (.) ...... a. (.) > C .0 >- C ::::, C) Q) 0 Q) Q) ro a. ::::, ::::, ro <( ro -, en 0 z 0 -, lL ~ ~ -, <( Month Figure 2.21: Time series of the calculated Junge coefficient, v', at the afternoon, from September 25 1996, to September 16 1997. The points are the monthly mean, and the line is the best fit of the monthly means. Chapter 2 Aerosol Optical Thickness Measurements ... 29 (2.11) mr is the atmospheric air mass, given by (Kasten, 1966) -1 ( 0.15 ) m = cos 0 + (2.12) r ( ) (93.885 - 0}1-253 The second term in this expression accounts for the curvature of the Earth, and contributes significantly only for 0 ~ 80°. Figure 2.22 illustrates the diurnal variation of aerosol optical thickness, Taer(A), as measured by the MFRSR, on March 19 1997, at 5 different wavelengths. Notice that local time is + 10 hours ahead of UTC time. The day was clear and sunny during the morning, but was interrupted by many cloudy breaks during the afternoon. Aerosol loading was fairly high during the morning, and dropped later in the afternoon. This drop in aerosol loading was more significant for the larger wavelength channels. Figure 2.23 is same as Figure 2.22, but for April 30 1997. The day was significantly clear and cloudless for most of the day, except for a small cloudy break during the morning. The aerosol loading was noticeably high and stable for the morning, but decreased steadily throughout the day. 2.3.4 Comparison of column and surface measurements 2.3.4.1 MFRSR and Nephelometer The aerosol optical depth Taer (A), is defined as the integral of extinction coefficient up to the top of the atmosphere, and is given by (2.13) Where z is the layer height, and /Jex,,p(A,RH) is the ambient aerosol extinction coefficient, which is the sum of the absorption and scattering coefficients. The aerosol scattering optical thickness 'fsca, at A= 502 nm, can be related to the measured scattering coefficient, /Jsp by an effective scaling height. Assuming that the aerosol is homogeneously mixed from 0 - Helf then (2.14) Chapter 2 Aerosol Optical Thickness Measurements ... 30 0.3 ....------,--415.4nm ~ --501.Snm 1' 0.25 --615.Snm .,. --672.7nm :i --869.Snm <1> 0.2 C ~ t.> £ 0.15 iii t.> ~a. 0 0.1 0 (/) e 0.05 ~ 0 20:00 22:00 0:00 2:00 4:00 6:00 8:00 Time (UTC) Figure 2.22: Diurnal variation of aerosol optical thickness, "taet{A), as measured by the MFRSR, on March 19 1997, at 5 different wavelengths. Local time is UTC + I 0 h. 0.25 ~ --415.4nm J 0.2 --501 .Snm vi (/) --615.Snm (I) C --672.7nm ~ 0.15 :ct.> --869.Bnm I- ~a 0.1 0 ~ 0.05 e(I) <( 0 21:00 23:00 1:00 3:00 5:00 7:00 Time (UTC) Figure 2.23: Diurnal variation of aerosol optical thickness, t 8 er(A), as measured by the MFRSR, on April 30 1997, at 5 different wavelengths. Local time is UTC + 10 b. Chapter 2 Aerosol Optical Thickness Measurements ... 31 where H efj'·IA) is the effective scale height, -rsea (A) is the scattering optical thickness, and fJsp (RH) is the ambient scattering coefficient at a given relative humidity, % RH. f3s/RHJ is calculated from the measured dry scattering coefficient, /Jsp,dry, at low relative humidity, using an enhancement growth factor f(RH) to correct for the hygroscopic growth of aerosol particles as f3sp(RH) = f(RH)x/Jsp,dry (2.15) The humidity correction is required, because the ambient aerosol measurement (MFRSR) of optical depth, is compared with the dry measurements of the nephelometer (details off(RH) calculation are in Chapter 4). (A) is calculated from the measured aerosol optical thickness as follows -rsea (2.16) where rn is the single scattering albedo (ratio of light scattering to total extinction). Average value of m(502)=0.74 was calculated using diffuse to direct ratio (Chapter 6). 2.3.4.2 MFRSR and TEOM The spectral variation of the aerosol optical thickness is mainly determined by the aerosol size distribution. The aerosol optical thickness is related to the size distribution by the following integral equation (2.17) where -r..,,(A) is the optical thickness at wavelength A, Qe is the Mie extinction efficiency factor, m is the complex refractive index, r is the particle radius and n(r) is the number of particles per unit area per unit radius in a vertical column through the atmosphere, with radii in the size range between r and r + dr. The total mass of particles in the entire vertical column of the atmosphere, or mass loading M (gm-2), is calculated as follows rb 4 M = pV = p f-t 1ir3n(r)dr (2.18) ra where p is the particle density in (g cm·3), V is the particle volume in the vertical Chapter 2 Aerosol Optical Thickness Measurements ... 32 column, and n(r) is the number of particles per unit area per unit radius in a vertical column through the atmosphere, and r0 ~ r 5.rb. A value of pat different relative humidity was calculated using the aerosol particles elemental composition, and substituted in Equation (2.18) (Chapter 4). n(r) is calculated using the 5 aerosol optical thickness measurements (Chapter 5). The columnar mass loading, M, is related to the surface mass concentration by M = mc(RH) x H eff (2.19) where mc(RH) is the ambient mass concentration, given by mc(RH) = mc(dry) x f(RH) (2.20) The TEOM PMlO collects particles smaller than 10 µm in aerodynamic diameter, which is equivalent to == 7 µm in geometrical diameter, d, using a particle density of p = 2.1. This agrees well with the maximum sensitivity range of the MFRSR of (0.2 ~ d ~8.0 µm). 2.3.4.3 Temporal variation Figures (2.24, 25, 26 and 27) and Figures (2.28, 29, 30, and 31) are plots of the morning and the afternoon daily averages of: the aerosol optical thickness, r at A = aer 502 nm, the average aerosol scattering coefficient, ,Bsp,d,y(km-1), measured by the integrating nephelometer, the dry aerosol mass concentration (µg m-3), measured by the TEOM, and the calculated total mass of particles in the entire vertical column M (g m-2). Although continuous measurement of nephelometer and TEOM were available, for comparison purposes, only measurements of the same days as were available for the MFRSR were used. It can be seen clearly that the daily pattern of the column measurements, was not always the same as that of the corresponding surface measurements. This is mainly because of the following reasons: the effect of particle hygroscopic growth caused by the relative humidity, the variation of the boundary layer throughout the day, and the vertical distribution of the aerosol particles. 2.3.4.3.1 Correlation of Nephelometer vs. TEOM Measurements of the nephelometer were noticeably low during the afternoon from May to August 1997, Figure 2.29. Similar low values were not observed by the Chapter 2 Aerosol Optical Thickness Measurements ... 33 ::l 0.25 Q) C .:.: .2 0.3) -5 «i ~ 0.15 0 'g 0.10 e Q) < 0.115 0.00-...... _ ...... _ ...... __...... _____...... ,_ ...... _ ...... ___...... ,...... __...... ___• SEJ> Q;t Figure 2.24: The morning daily average of the aerosol optical thickness, T at A = 502 nm, measured by the MFRSR. aer _ o.1a--...... - ...... --...---- ...... --.----- ...... --.------l 0.14 'E -~ 0.12 (.) ~ 0.10 8 gi 0.00 ~ 0.00 ~ 1/) 0.04 0 e0.02 Q) < 0.00 -l"-"-...... "+-" ...... -"-+----"-"'-"+ ...... '1--- ...... ""'4- ...... "'""'--l--'- ...... -Jl&-i ...... llll+,l...ill.l.~IUllf-.,___.,..J. SEJ> Q;t Figure 2.25: The morning daily average of the aerosol scattering coefficient, ,8,p.d,y(km-1), measured by the integrating nephelometer. Chapter 2 Aerosol Optical Thickness Measurements ... 34 Figure 2.26: The morning daily average of the dry aerosol mass concentration (µg m"3), measured by the TEOM. Q10+-...--+--.---+---,,-.-.---+--...--+--.---+----~--+-----+----+---.-+--....--+-.- 0.CB wQ(B ~ Q(Jl en :g Q(E ~ Olli § Qot 0 u Q(B iii o 002 1- 001 000+-'----"+-'...... --+- ...... 'l- ...... -+-.....&.l--'f--- ...... '+-'--"'-+- ...... ~...... "'l"-'- ...... -+-----l'--""-.._-+-'- S:J> Qi jj Figure 2.27: The morning daily average of the calculated total mass of particles in the entire vertical column M(g m-2). Chapter 2 Aerosol Optical Thickness Measurements ... 35 tJ) ~ O.a:J C: .::.! (.) £ 0.15 ~ a o 0.10 0e ~ a.a:; 0.00 -Jl-ll,...... -'-l-'- ...... u..+-__."""""'1,...... "1-- ...... ""'f-o ...... """'1- __.....,..,...... _.,._ ...... ~ Qj Ju Figure 2.28: The afternoon daily average of the aerosol optical thickness, r at A = 502 nm, measured by the MFRSR. aer ~ o.10--...... - ...... --.--- ...... - ...... --.--- ...... - ...... --.--- ...... ----,,--..,.....--.. o.oo .::.!E :;:- O.CB C: -~ 0.(51 !E ~ 0.00 (.) Cl a.a:; C: ·55 0.04 t:: ~ O.CB tJ) o 0.02 tJ) 0.01 eQ) <( 0.00 -J'-1,l,...... -'-l-'- ...... ---~--...'l--"'--'4.ll'---"1 ~ Qj Ju Figure 2.29: The afternoon daily average of the aerosol scattering coefficient, /:J,p.d,y(knf1), measured by the integrating nephelometer. Chapter 2 Aerosol Optical Thickness Measurements ... 36 (')45. .§ 40. ~ C: 35. g ~ 3'.l "E ~ 25. C: jv,4 8 a>...... I 11 1/J V, I 1,...... _... • I ,;-,..,. ... ~ 15. I" I T --' ' & .... II l I l ' ..,,.. I I ~ 1Q 11 !1 0 11 11 i 1/J ! I e 5. I Q) i I i I I ii 11 ~ (lJ 0. I I I ~ Qi Ju Figure 2.30: The afternoon daily average of the dry aerosol mass concentration (µg m"3), measured by the TEOM. QOO-J1-1,J-.llllll..+u...i..1.1..i..+-...a..1.11J1i-..llUII..L.ll\----"'L.i..,~=l4---il=Wf--.....i....i+,11,.11...1JL..J+i,.J....i!--J/LIJwi..JJ.aµ....._+-'- ~ Qi Ju Figure 2.31: The afternoon daily average of the calculated total mass of particles in the entire vertical column M(g m"2). Chapter 2 Aerosol Optical Thickness Measurements ... 37 TEOM or by MFRSR for the same period of measurements. No problem was reported by the NSW-EPA regarding the nephelometer measurements. However, it was necessary to investigate any possible underestimation of scattering measurements during that period. Aerosol scattering coefficient, /3sp,dry(10 km- 1), was compared to TEOM mass concentration measurements (µg m-3). Figure 2.32 and Figure 2.33 are plots of /Jsp versus mass concentration, measured during the morning and afternoon respectively. Both plots included two sets of measurements, the first is from September 96 to April 97, and the second set is measurements from May to August 97. The straight line is the regression fit of each set of data. For the first set (September 96 to April 97), the data points of the morning were less scattered than the afternoon, with correlation coefficient of R2 =0.5 and R2 =0.3. The slope was 0.017 for the morning and 0.009 for the afternoon. A possible source for this difference between the morning and afternoon could be the change of relative humidity (90 ± 12% for the morning and 82 ± 16 % for afternoon). There have been reports of loss of semi-volatile aerosol particles due to the use of heater in the nephelometer (Bergin et al., 1997), and the TEOM (Allen et al., 1997; Ayers et al., 1999). Semi-volatile materials are compounds that transfer between gas and condensed phases. These include ammonium nitrate, semi-volatile organic compounds, and particle bound water. Allen et al. (1997) suggested that the semi-volatile fraction of PM2.5 is larger than in PMIO. Since the nephelometer measurements are related to particles of less than 2.5 µm, the loss of semi-volatile particles of the nephelometer is expected to be larger than in TEOM PMIO. There is more evidence to suggest that the loss of the semi-volatile particles is larger when the relative humidity is low. Another possible explanation could be that a substantial portion of the particles size was larger than 1 µm during this period. Anderson et al., 1996, found that forward truncation errors increases systematically with particle size, (20% - 50%), for coarse mode particles. Although this explanation may account for some of the under estimation of scattering coefficient, it means that particles size was consistently larger during the afternoon than the morning. However, morning and afternoon aerosol particle size distribution, presented in Chapter 5, do not support this explanation. As for measurements from May to August 97, a significant drop in the nephelometer measurements for the afternoon can be seen clearly in Figure 2.33, as the slope decreased to 0.005. The data points of this period were significantly below Chapter 2 Aerosol Optical Thickness Measurements ... 38 2 '7- 0 Sept 96-April 97 0 0 E May 97-Aug 97 + (Sept 96-April 97) ~ 1.6 - • - - • · linear (May 97-Aug 97) 0 ---linear (Sept 96-April 97) y = 0.0174~t084 ..... R2 = 0. 1 0 -C -Q) (May97-Aug 97) 1.2 0 ·o y = 0.0096x + 0.005 0 0 Ii= 2 Q) R =0.4967 0 0 0 0.8 0 C) 0 0 C O + ·c: 0 0 + ...... ~ Q) o .... + :i:: 0.4 <13 · Mass concentration (µgm-3) Figure 2.32: Relationship between the morning average of the aerosol particle scattering coefficient, /Jsp,t1,y(101cm·'), measured by the nephelometer, and mass concentration (µg m·3), measured by the TEOM PMI0. The circles are measurements from September 96 to April 97, while the plus are measurements from May 97 to August 97. Chapter 2 Aerosol Optical Thickness Measurements ... 39 1 ~ 0 Sept 96-April 97 -' E + May 97-Aug 97 (Sept 96-April 97) ~ 0.8 • - • • · · Linear (May 97-Aug 97) y = 0.0087x + 0.0336 0 ..- ---Linear (Sept 96-April 97) R2 = 0.3021 .... -C: 0 Q) 0.6 ·o !f= Q) 0 0 0.4 0) C: ·;::: :::Q) 0.2 ro en0 0 0 20 40 60 Mass concentration (µgm-3) Figure 2.33: Relationship between the afternoon average of the aerosol particle scattering coefficient, /Jsp,dry(IO 1cm·1), measured by the nephelometer, and mass concentration (µg m·3), measured by the TEOM PMI0. The circles are measurements from September 96 to April 97, while the plus are measurements from May 97 to August 97. Chapter 2 Aerosol Optical Thickness Measurements ... 40 the bulk of data points measured during September 96 to April 97. This clearly indicates a problem in the nephelometer measurements for this period. Measurements during the morning in Figure 2.32, showed a slight decrease in the relationship slope too, but it was less significant than the afternoon, as data points were within the range of the observed measurements for the period of September 96 to April 97. It is obvious that there is a systematically lower than expected nephelometer response during the afternoon. This could be related to the volatilisation of semi-volatile aerosol components, caused by a probable over-heating of the sample. The loss of particles during this period is much more significant than in previous measurements (Sept. 96 to April 97). The exact reason for such behaviour is not known. Possible reasons could be the increase in the heater temperature or the presence of more semi volatile material during this period. As a result, the nephelometer measurements made from May to August 1997, for the afternoon period, will be excluded from the scale height analysis. 2.3.4.3.2 Determination of effective scale height To investigate the variation of the boundary layer height, Heffwas calculated using the two sets of corresponding parameters, Tsca(A) I /Jsp(RH) and M /mc(RH). Figure 2.34 and Figure 2.35 are plots of the calculated morning and afternoon Heff (km) time series, estimated using scattering and mass measurements respectively. The average effective scale height derived from scattering measurements was higher during the afternoon (2.64 km) than the morning (2.00 km). On the other hand, the obtained average scale height value using mass measurements for the morning was in good agreement with the afternoon one (1.59 and 1.73 km). The majority of Heff values varied from few hundreds of meters up to 8 km. Table 2.3 is a summary of the obtained effective scale height Heff (km), using the scattering and mass measurements, for the morning and afternoon, and its standard deviation ( a). A scatter plot of the Heff derived from scattering measurements versus the value derived using mass measurements is shown in Figure 2.36 and Figure 2.37 for both the morning and the afternoon. The dotted line is the regression best fit. The agreement between the two values was excellent for the morning, (slope= 0.94, R2 = 0.68), although it showed a systematic overestimation of the scale height derived from scattering measurements over the value derived using the mass measurements. For the afternoon, the agreement was not as good as the morning, (slope= 1.54, R2 = 0.43), and the overestimation of Chapter 2 Aerosol Optical Thickness Measurements ... 41 100 =------======o= E ~ 10 It: CD :t:... .i:::. 1 ·a;O> .i:::. Q) ro(.') 0.1 Cl) 0.01 +--...._-+---'-----,-----'---+------+-...... --+----'----' Sep Nov Jan Mar May Jui Month Figure 2.34: Time series of the effective scale height H,.ff(km), estimated using the MFRSR aerosol optical thickness, -r , and the Nephelometer scattering coefficient, Psp, for both the morning and aer afternoon periods. 10.00 -E ~ It: CD :t:... .i:::. 1.00 ·a;O> .i:::. Q) ro(.') Cl) ------0.10 ____...... xPM ___ ...._ ___.______, Sep Nov Jan Mar May Jui Month Figure 2.35: Time series of the effective scale height H,.ff(km), estimated using the calculated total column mass, M, and the PMl0 TEOM mass concentration, for both the morning and afternoon periods. Chapter 2 Aerosol Optical Thickness Measurements ... 42 y =0.94x + 0.77 ' C) R2 =0.68 ' -C 0 ·.::: 6 0 Q) +-' +-' ' ' co 0 0 (.) ' ' Cl) 0 .' 0 ' -+-' 4 ' ' .c ' C) ' ' ·a5 C:8' 0 .c £foo . Q) 6)'0 ,,-6 0 co 2 . (.) Cl) 0 0 2 4 6 8 Scale height (Mass) Figure 2.36: Relationship between the morning effective scale heights H,JJCKm) estimated using mass measurements (MI mass cone. (RH)) and H,ff (km) estimated using scattering measurements ( -Z:.ca I /3,p(RH)). The dotted line is the regression line of best fit. Solid line is I: I. 10 y = 1.54x + 0.64 X C) .··R2 = 0.43 -C 8 ·;:: X Q) +-' +-'co X •• (.) X X • Cl) 6 X +-' *X.· -.c X )S• C) . ·a5 4 .c Q) co (.) 2 Cl) X 0 0 2 4 6 8 10 Scale height (Mass) Figure 2.37: Relationship between the afternoon effective scale heights H,JJ(km) estimated using mass measurements (MI mass cone. (RH)) and H,JJ (km) estimated using scattering measurements ( Tsca I /3,p(RH)). The dotted line is the regression line of best fit. Solid line is 1: 1. Chapter 2 Aerosol Optical Thickness Measurements ... 43 Table 2.3: Summary of the calculated effective scale height H,ff(km), using the two different parameters, scattering and mass measurements, for the morning and afternoon. MI mass cone. (RH) Period s.d. (a) s.d. (a) AM 2.00 1.48 1.59 1.42 PM 2.64 2.17 1.73 1.21 Chapter 2 Aerosol Optical Thickness Measurements ... 44 the scattering He.ff was larger for this period than the morning. We believe that this overestimation is very likely originated from loss of semi-volatile particles, which systematically caused a lower scattering coefficient values for the afternoon in comparison to the TEOM PMl0. 2.3.4.4 Diurnal variation Diurnal variations of the aerosol optical thickness, T , the nephelometer scattering aer coefficient, /Jsp (dry and ambient in km- 1), and the estimated effective scale height He.ff (km), for selected days, are presented in Figures (2.38, 39, 40, 41, and 42). Sharp peaks observed in T measurements were caused by passing clouds, and are not aer included in He.ff calculations. Observations on March 19 and May 13, 1997, resulted in He.ff that followed a similar pattern throughout the day, but with different values. He.ff was low during the morning and increased later during the afternoon to reach a peak. He.If ranges from 0.06-2 km, Figure 2.38, and 1.0-5.5 km, Figure 2.39. This scale height pattern in Figure 2.38 is similar to a typical boundary layer or mixing depth height behaviour under sunny conditions. The boundary layer usually extends 1-2 km above the heated ground, until it is abruptly halted by a stable, capping layer. While for a clear night, a stably stratified layer develops at the ground, extending upward several hundreds meters to the top of the surface temperature inversion (Berman et al., 1997). In the early morning following the sunrise, surface heating breaks through the temperature inversion, and the mixing depth grows rapidly. In Figure 2.39 it showed the same pattern, but He.ff Was greater than the typical boundary height of 1-2 km. This was partially affected by the loss of semi-volatile components, and hence increased Helf value. However, the high value means that aerosol is not necessarily confined to first 1-2 km of atmosphere, as previously thought. Leitere et al. (1997) presented results of vertical profile of spectral aerosol optical thickness and extinction coefficients. Measurements revealed that haze can be found to up to heights of 5 km. Skouratov et al. (1997) reported that the haze aerosol vertical distribution had a complicated multi-layered structure, where three and more layers were observed over the whole troposphere. The majority of particles were confined to the surface layer. There were two layers at altitudes of approximately 2 - 3 and 5 - 6 km and a subtropopause layer. Measurements from the TARFOX campaign (Russell et al., 1999a; and 1999b) showed that aerosol did exist to up to 5 km as well. Seasonal Chapter 2 Aerosol Optical Thickness Measurements ... 45 0.4 --...... -- ...... -- ...... -- ...... ----.-----.--...- 2.5 0.35 - tao(500) i Q. ~ ea. - Bsp(dry) 2 en - 0.3 ~ C -e-Bsp(ambient) C: .!!? ~Heff .:rt. u 0.25 1.5 .2 le .s::. Q) ::: 8 0.2 -~ Cl 1 a..5 0.15 0 ! 0.1 ~ ~ 0.5 e en 0.05 ~ 0------~o 20:00 22:00 0:00 2:00 4:00 6:00 8:00 Time (UTC) Figure 2.38: Diurnal variation of the aerosol optical thickness, -r , the nephelometer scattering aer coefficient, /Jsp (dry and ambient in 1an· 1), and the estimated effective scale height Heff(km), for March 19 1997. Local time is UTC + I O h. 0.12 ------+---.------6 - tao(500) 0.1 - Bsp(dry) 5 -e-Bsp (ambient) 0.08 4 E ~ 0.06 3 0.04 2 0.02 1 0------0 21 :00 23:00 1:00 3:00 5:00 7:00 Time(UTC) Figure 2.39: Diurnal variation of the aerosol optical thickness, -r , the nephelometer scattering aer coefficient, /Jsp (dry and ambient in km"1), and the estimated effective scale height Heff (km), for May 13 1997. Local time is UTC + 10 h. Chapter 2 Aerosol Optical Thickness Measurements ... 46 dependence of the vertical distribution of particles was found by (Hanel, 1998), who studied the aerosol optical thickness vertical profile of up to 6 km, and his results showed that aerosol particles vertical distribution depend not only on particle types, but also very strongly depend on the season. He.ff values derived for October 30 1996, Figure 2.40, and April 4 1997, Figure 2.41, show different pattern from the previous cases. On October 30 1996, He.ff Was high during the morning, and dropped significantly during the day, (2.6 to 1 km). While on April 4 1997, the scale height was noticeably stable during the day, but showed a sharp peak for two hours during the early hours of morning (8 to 2 km). On March 20 1997, Figure 2.42, Heffwas fairly stable throughout the day, and peaked at the end of the day (1.5 to 2.5 km). The scale height for the last two cases, Figure 2.41 and Figure 2.42, followed closely the aerosol optical thickness's behaviour, mainly because .Bsp(ambient) was relatively constant during the measuring period, and changes in aerosol loading did not occur near the surface. Such variations in the scale height behaviour illustrate the strong temporal and vertical variability of the aerosol distribution. Other workers who have calculated the scale height, using vertical to surface measurements found similar values of 2.5 to 8 km (Esposito et al., 1996), and 2.32 to 5.99 km (Pandithurai et al., 1997). Results of the effective scale height presented above, show that the often used approach of a homogeneous distributed aerosol within the surface boundary layer of 1-2 km, in which more than 80 % of the total aerosol is concentrated, does not always account for the observed vertical loading of aerosol particles. However, it does suggest that aerosol may be concentrated in several layers. Nonetheless, the obtained averages of 2 and 2.6 km from scattering measurements and 1.6 and 1.74 km from mass measurements indicate that the majority of aerosol particles are concentrated at or near the surface. In order to achieve an accurate correlation between surface measurements such as the aerosol scattering coefficient or mass concentration, and radiometric column measurements, a detailed vertical profile of the aerosol loading is required, using balloon or aircraft flights. A more accurate estimate of the lost semi-volatile components of the aerosol in situ dry measurements will also be required for such correlation, even though many suggest that the loss does not exceed 20 %. However, since different in situ instruments use different heating temperatures, and different Chapter 2 Aerosol Optical Thickness Measurements ... 47 0.1 3 ea 0.09 - tao(500) :. - Bsp(dry) .. ~ 2.5 0.08 -&-Bsp (ambient) ~~ tJi ~ Haff en 0.07 Q) 2 'EQ) C :it: ·c:; 0.06 E 0 .li&::: le 0.05 1.5 £ 2l lt:: (II 0 Figure 2.40: Diurnal variation of the aerosol optical thickness, -. , the nephelometer scattering aer coefficient, psp (dry and ambient in 1cm·1), and the estimated effective scale height H,g(km), for October 30 1996. Local time is UTC + JO h. 0.12 10 ea - tao(500) ii Q. .,. ea. 0.1 _..,Bsp(dry) 8 tJi -e-Bsp(ambient) en 'E Q) Q) 0.08 ~ Haff C: ·c:; :it: 6 E 0 le .li&::: Q) € 0 (.) 0.06 lt:: Time(UTC) Figure 2.41: Diurnal variation of the aerosol optical thickness, -. , the nepbelometer scattering aer coefficient, Psp (dry and ambient in 1an·1), and the estimated effective scale height H,g(km), for April 4 1997. Local time is UTC + 10 h. Chapter 2 Aerosol Optical Thickness Measurements ... 48 0.2 4 ~ - tao(500) 3.5 J cl 0.16 ----Bsp(dry) vi _.,_ Bsp(ambient) 3 en 'E Q) Q) -M-Heff C: .:,,t_ ·o 2.5 (.) 0.12 :c = E 2 .:,,t_ iii- 8 (.) C) 0.08 :;c. ·C- 1.5 0 c6 - :i::: 5l ~ 1 e en 0.04 Q) «I 0.5 0 0 20:00 22:00 0:00 2:00 4:00 6:00 8:00 Time(UTC) Figure 2.42: Diurnal variation of the aerosol optical thickness, -r , the nephelometer scattering 00 coefficient, /Jsp (dry and ambient in 1an·1), and the estimated effective scale height Heff(km), for March 20 1997. Local time is UTC + 10 h. Chapter 2 Aerosol Optical Thickness Measurements ... 49 sites have different contributing sources of aerosol, this means that the loss will be dependent on the site and measuring instrument. The use of ambient measurements will eliminate the need of correction for particle growth and the loss of particles caused by heating the sample, and will improve the accuracy of such correlations. 2.4 Summary and conclusion Results of Multifilter Rotating Shadowband Radiometer (MFRSR) measurements of in Sydney were presented to provide more information about the long-term variability of spectrally resolved aerosol optical thickness, as well as the diurnal variability of aerosol. The highest mean of the monthly average of aerosol optical thickness, for the morning and afternoon, was observed in January, and summer was the season which had the highest mean value. Aerosol column measurements of the MFRSR were compared to surface measurements of the nephelometer and TEOM PMIO µm. An average value of an effective scale height of 2 and 2.6 km from scattering measurements and 1.6 and 1.74 km from mass measurements, for both the morning and afternoon, indicate that the majority of aerosol particles are at or near the surface. The temporal and diurnal variability of the observed scale height, reflect the difficulty of estimating vertical aerosol optical thickness from surface scattering coefficient measurements, without prior knowledge of the vertical profile of the boundary layer. There is more evidence to indicate that aerosol particles, in many cases, exist in several altitude layers. In order to improve the correlation between surface measurements, such as the aerosol scattering coefficient or mass concentration, and radiometric column measurements, a detailed vertical profile of the aerosol loading is required, using balloon or aircraft flights. A more accurate estimate of the effect of the loss of the semi-volatile components of the aerosol dry measurements will also be required to improve the correlation between column and surface measurements. Using ambient in situ measurements will eliminate the need of correction for particle growth and loss, and will improve the accuracy of such closure studies between column and surface measurements. Chapter 3 Aerosol Light Absorption Measurements 3.1 Introduction Atmospheric aerosols are the dominant contributor to the absorption of light in the visible and near infrared region. It is black carbon, the main constituent of soot, also referred as elemental carbon or free carbon, which is almost exclusively responsible for the light absorption of the particles. Black carbon particles are a residue from incomplete combustion of organic materials. The main anthropogenic sources are the burning of fossil fuels and automotive emission. Forest fires and biomass burning, are significant contributors too. Thus it is of a great importance to obtain reliable data on black carbon absorption coefficient and concentration in different areas and with a good time resolution. Such data are needed for climate and radiative transfer models. A critical factor in relating optical absorption of particles and its mass is the specific mass absorption coefficient or efficiency, defined as the ratio of absorption coefficient to the mass concentration. Unfortunately, the value of this factor is not well known, since values reported in the literature vary significantly, from 2 to 25 m2i 1 (Horvath, 1993a, and Liousse et al., 1993). Reasons for differences may be due to the use of different measuring techniques, or different types of aerosol considered. Theoretical calculation of the specific mass absorption coefficient using Mie theory shows that it depends very much on refractive index, mainly the imaginary part, particle density, and the size distribution of the particles. Internally mixed aerosol will give a higher absorption coefficient than an external mixture. In this chapter, we present experimental measurements of the absorption coefficient of particles, derived from transmission measurements of test carbon and ambient aerosol samples collected on Teflon filters, using the Laser Integrating Plate Method (LIPM). Samples were collected on Nuclepore filters too. Measurements are validated using the reflectometer. Results are used to determine a more appropriate value of the specific mass Chapter 3 Aerosol Light Absorption Measurements. 51 absorption coefficient both experimentally, and theoretically using Mie theory calculation. The chemical composition of the aerosol samples is measured using accelerator based Ion Beam Analysis (IBA), which will provide valuable information about the black carbon mass. It is also used to calculate particle density and refractive index of aerosols, required for theoretical calculations. 3.2 Theory and calculation 3.2.1 Mie calculation theory 3.2.1.1 Complex refractive index and absorption of electromagnetic energy Bohren and Huffman (1983) presented concepts and equations from electromagnetic theory that deal with the problem of absorption and scattering of light by small particles. Here we are presenting a brief outline of the relevant theory and formulation given. For an electromagnetic field (E,H), the Poynting vector S = E x H specifies the magnitude and direction of the rate of transfer of electromagnetic energy at all points of space; it is of fundamental importance in problem of propagation, absorption and scattering of electromagnetic waves. For an electric field E, and a magnetic field H, the plane electromagnetic wave is given by (3.1) (3.2) where the wave vector k takes the complex form k= k'+ik" (3.3) Eo and Ho are constant vectors, while w is the angular frequency. The plane waves in (3.1) and (3.2) are compatible with Maxwell equations provided that k, E0 and H0 are perpendicular so that Chapter3 Aerosol Light Absorption Measurements. 52 The wave vector must also satisfy this equation can be written as k'2 - kn + 2ik' • k" =a/£µ (3.4) where £µ is the property of the medium in which the wave propagates, µ is the permeability and £ is the complex permittivity. The vectors k' and k" are properties of the wave. (3.5) £0 is the permittivity of free space, X is electric susceptibility, and a is the conductivity. For a homogeneous wave, the wave vector can be written as wm k = k'+ik" (3.6) C c is the speed of light, m is the complex refractive index (3.7) For a free-space wave number, m I c = 21'l I A, the plane homogeneous wave will have the form -21'Ckz) (i21'tl'lz ) (3.8) Ee= E0 exp (-A- exp -A--imt, A is the wavelength of light, z = e • x, e is the real unit vector in the direction of propagation. The imaginary part of the complex refractive index determines the attenuation of the wave as it propagates through the medium, and the real part determines the phase velocity v = c In. Chapter 3 Aerosol Light Absorption Measurements. 53 3.2.1.2 Absorption of electromagnetic energy The Poynting vector for a plane wave is S=-Re{ExH*}=Re1 {Ex(k*xE*)} --"-----'-, (3.9) 2 2mµ* where Ex (k * xE *) = k * (E • E *)- E * (k * -E) the asterisk denotes the complex conjugate. If the wave is homogeneous, then k • E = 0 and k * • E = 0 , for a wave propagating in the e direction, then we have (3.10) The magnitude of S is called the irradiance or intensity I, which is energy per unit area and time. As the wave traverses the medium, the irradiance is exponentially attenuated: (3.11) and 41Zk a=-- (3.12) a ;i, a0 is the absorption coefficient, 10 is the irradiance at z = 0. This means that the rate at which the electromagnetic energy is removed from the wave as it propagates through the medium is determined by the imaginary part of the complex refractive index. For a measured 10 and I at z = 0 and z = X, then aa, and k can be determined (in principle) using (3.13) Chapter 3 Aerosol Light Absorption Measurements. 54 3.2.1.3 Mie scattering for a single sphere Mie theory provides a solution to Maxwell's equations for the case of absorption and scattering of light by small spheres. The equations of particular interest here are the cross-sections and efficiencies. The scattering cross-section ( Cs) is the ratio of the rate at which energy is scattered across a surface to the incident irradiance, and has the dimension of area. The scattering efficiency, Qs = Cs I (cross-sectional area), represents the fraction of the energy scattered by a sphere per unit cross sectional area. It is a dimensionless quantity. Similar definitions apply for the absorption cross-section Ca, and absorption efficiency Qa, Extinction is the sum of absorption and scattering, and again equivalent definitions apply. These equations depend on refractive index, m = n - ik, and the relationships between them are given below. Qs =-2Cs =-22 L...J(2/+l)(a,~ I 12 + Ib, 12 ), (3.14) 7tr X /;J C 2 - Qe = ~ = - 2 L(2l + l)Re{a1 + b1} (3.15) 7tr X M (3.16) where I= 1,2,3, ..... N, is index of summation a1 and b1 are Mie coefficients, given as (3.17) b, l/f (mx)l/f;(x)-ml/f (x)l/f;(mx) = 1 1 (3.18) l/f 1(mx )i;;(x)- mi;1(x)l/f;(mx) where Chapter 3 Aerosol Light Absorption Measurements. 55 ,rz-I lfl n (z) = (-)2 J 1 (z) (3.19) 2 n+- 2 (3.20) are called Ricatti-Bessel functions, J and Hare Bessel functions. The advantage of the efficiency factors is that they are functions only of the size parameter x = 2nr I A, and the refractive index, rather than the radius rand wavelength separately. Wavelength is sometimes assumed to be constant (which is not exactly true), allowing us to plot Q as a function of r. 3.2.1.4 Attenuation extinction coefficient There are a three ways of representing extinction of a sphere: Cross section Ce (3.21) Cross section per unit area Cel A (3.22) Cross section per unit volume Cel V (3.23) For a beam traversing a distance through a medium, the attenuation coefficient (3.24) where rJ is the number of particles per unit volume, l/TJ is the average volume allocated to a single particle, and the volume fraction, f, of particles is rJV where v is the volume of single particle. We can write the volume attenuation coefficient au as C a =-e (3.25) V V ' The mass attenuation coefficient am, which is the extinction cross section per unit particle mass is given by Chapter 3 Aerosol Light Absorption Measurements. 56 (3.26) where p is the density of the particle. The extinction cross section per unit volume or mass, rather than the cross section per unit area, is of great importance, for it tells us how effective a fixed mass of a particles is in removing light from a beam. In the case where absorption is the dominant component of extinction, then au for a sphere is 3Qal 4r, from (3.26), and then we can define (ja 3Qa €=-=-- (3.27) me 4rp e is specific mass absorption coefficient, me is the particle mass concentration. This equation will be used later in theoretical calculation of e. 3.3 Experimental measurements In 1991 Australian Nuclear and Science Organization, (ANSTO), Physics Division, set up and operated a fine particles aerosol sampling network, known as the Aerosol Sampling Program (ASP). The ASP network, which covered an area up to 200 km from the centre of Sydney, determined the elemental composition of these particles. Samples were collected every Sunday and Wednesday at each sampling site. (ERDC, 1995) 3.3.1 PM2.5 sampler The standard Aerosol Sampling Unit, which was built by ANSTO, consists of size selective inlet; a cyclone to provide a particle size cut-off based on the flow rate; an adjustable orifice that controls flow rate; and a vacuum pump, which produces the flow. A magnehelic and vacuum gauges are used to monitor the flow rate. The cyclone operates with a flow rate of 21.8 1/m to collect particles less than 2.5 µm in aerodynamic diameter. 25-mm stretched Teflon filter was used to collect the aerosol particles. A schematic diagram of the sampling unit is shown in Figure 3.1. Chapter 3 Aerosol Light Absorption Measurements. 57 STACK CAP PROGRAMMABLE CLOCK CONTROLLER STACK PUMP MAINS ELECTRICAL /SOCKET CIRCUITRY MAINS POWER INLET ) CYCLONE TO VACUUM PUMP INLET ADJUSTABLE ORIFICE Figure 3. 1: A schematic diagram of the sampling unit PM2.5 µm. Chapter 3 Aerosol Light Absorption Measurements. 58 Another model of the ASP unit, uses a two stage Nuclepore Polycarbonate filters to collect fine particles of less than 2.5 µm and particles of diameters between 2.5 and 10 µm respectively. 3.3.2 Analysis techniques Accelerator based ion beam analysis (IBA) has been proven to be a reliable and fast technique of analysing aerosol particles elemental composition with sufficient sensitivity, and no sample preparation is required. It allows a non-destructive, simultaneous analysis of the vast majority of elements of interest in pollution studies, for a very large number of samples. 3.3.2.1 Accelerator based Ion Beam Analysis (IBA) The filter samples were analysed at ANSTO by the Ion Beam Analysis Technique (IBA) using the 3MV van de Graaff accelerator. Typical beam currents of 10 nA over a diameter of 8 mm were used, the currents were kept below 0.2 nA I mm2 to avoid damaging the filters, and to avoid any elemental loss. Run times under these conditions were of the order of 5 minutes per filter for full analysis. The ion analysis techniques used simultaneously throughout this study were~ 3.3.2.1.1 PIXE Particle Induced X-Ray Emission, is used to analyse the following elements: Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Cu, Ni, Zn, Br, and Pb from few ng m-3 upwards. Each element present in the sample is induced by a proton beam to emit X-Rays whose energy is characteristic of the element and whose number is proportional to the mass concentration of the element. Figure 3.2 shows the energy of Ka, La, and MaX-Rays as a function of atomic number. One disadvantage of the PIXE analysis is that it is not applicable for the lighter elements with atomic number smaller than 13 (Al), such as C, N, H, and 0. An example of an aerosol loaded and a blank Teflon filter spectrums are shown in Figure 3.3 (a and b).The loaded filter was exposed in Mascot, July 9, 1997. The PIXE method was used to determine elements from Al to Pb, where each peak represents the element. The concentration of each element is determined by comparing the area of the peak for the loaded filter with the blank filter. Chapter 3 Aerosol Light Absorption Measurements. 59 ~ :Sn AnOdj Kf 20 ;i. I -1: 1/JIO ZI' - ._') ;/_ ~IU C LI.I N• 11),, 10 ,.Iii.~. ~ lo4 ...~ ~ .. r >C .. !l(l, L/ ' ... W'- ci.i!- ,;1 f.1 II H1 l~ A\ liill ot. .. I~ ~·c1 c. 111, ~ 11 r.s,- I ,- I I ~ 0 0 10 20 30 40 50 60 70 80 Atomic Number. Z number. From (Kasahara, 1999). Figure 3.2: Energy of Ka, La, and Ma X-Ray as a function ofatomic Chapter 3 Aerosol Light Absorption Measurements. 60 1.E+3 .23 C ::J 1.E+2 0 (.) 1.E+1 1. E +0 +---...... -'-+-.._...... __,...... --+-<....1....1...... ,_ ...... Lf-L- ...... ""'+-u..aa--i ...... Ul&&.l"+-'-..IL.l.-'-"f-"'-'-..L....U..f- O 100 200 300 400 500 600 700 800 900 1000 Channel number Figure 3.3 (a): Typical PIXE spectrum for Teflon filter collected in Mascot on July 9, 1997. 1.E+2 rn -C ::J 0 (.) 1.E+1 1.E +0 +--...... "'--I- ...... """"-+ ...... '+-'-"-' 0 100 200 300 400 500 600 700 800 900 1000 Channel number Figure 3.3 (b): Same as (a) but for a blank filter. Chapter 3 Aerosol Light Absorption Measurements. 61 3.3.2.1.2 PESA Proton elastic scattering analysis is used to analyse Hydrogen, H, at levels down to 20 ng m-3• The protons in the cyclotron beam that are elastically scattered by hydrogen into a forward angle of 30° are counted to give a measure of the amount of hydrogen in the sample. Unfortunately, PESA analysis could not be run on Nuclepore filters. Figure 3.4 (a and b) are plots of typical PESA spectrum for a loaded aerosol and a blank filters. 3.3.2.1.3 PIGME Particle Induced Gamma ray Emission, is useful for the analysis of light elements, such as Li, B, F, Na, Mg, Al, and Si. However, since PIGME originates from a nuclear process rather than an atomic reaction, it is less sensitive for aerosol filter analysis than PIXE; it can only detect concentrations above 100 ng m-3• It is usually reliable for Sodium measurements. Figure 3.5 (a and b) are typical PIGME spectrum for a loaded aerosol and blank filters. Figure shows zero concentration of sodium on that day. 3.3.2.2 Determination of aerosol types Elements of aerosol particles were assumed to occur in particular forms of chemical composition, such as oxides, sulfate or sea salts. Malm et al (1994) define the fine aerosol species at most continental sites to be classified into five major types: sulfates, nitrates, organics, elemental carbon and soil. Sea salt and non-soil potassium are the other species included in the model. Species concentrations were calculated by multiplying the elemental concentration by a multiplicative molar correction factor. This factor is determined by an accounting of the total molar weight of the assumed species, then by dividing by the molar weight of the element. The species used are given below. Sulfate All sulfur is assumed to be from sulfate, which exists in the form of ammonium sulfate (NRi)2S04. [Sulfate] = 4.125 [S] Chapter 3 Aerosol Light Absorption Measurements. 62 1.E+5 1.E+4 Ul 1.E+3 "E ::::, 0 (.) 1.E+2 1.E+1 1.E+0 0 100 200 300 400 500 600 Channel number Figure 3.4 (a): Typical PESA spectrum for Teflon filter collected in Mascot on July 9, 1997. 1.E+5 1.E+4 ~ 1.E+3 C: ::::, 0 (.) 1.E+2 1.E+1 1.E+0 0 100 200 300 400 500 600 Channel number Figure 3.4 (b): Same as (a) but for a blank filter. Chapter 3 Aerosol Light Absorption Measurements. 63 1.E+4 00 1.E+3 "E ::, 0 o 1.E+2 1.E+1 1.E+0 +-~~~'------t---'---'-----'-~--+-~~~~-+-~~~~t----'---'---'----'---+ 0 100 200 300 400 500 Channel Number Figure 3.5 (a): PIGME spectrum for Teflon filter collected in Mascot on July 9, 1997. 1.E+4 .l!l 1.E+3 C ::, 0 o 1.E+2 1.E+1 1. E +0 +-~~~'------t---'---'-----'-----'---+-~~~~-+-~~_,__,__f---J__,____.____._--+ 0 100 200 300 400 500 Channel Number Figure 3.5 (b): Same as (a) but for a blank filter. Chapter 3 Aerosol Light Absorption Measurements. 64 Soil Soil mass is calculated from the sum of masses of elements associated with soil, in form of oxides. FeO and Fe20 3 are equally abundant, and [soil K] = 0.6 [Fe]. [Soil] = 2.2 [Al] + 2.49 [Si] + 1.63 [Ca] + 2.42 [Fe] + 1.94 [Ti] Organic carbon It is assumed that all nitrate and water volatilise during exposure to vacuum while the filter is being analysed, so as both don't contribute to the mass of H, which is divided between sulfates and organic carbon (OMC). The contribution ofH in ammonium sulfate is derived from the H / S ratio, 8 / 32 or 0.25. A fraction of hydrogen contribution in organic mass /oH was calculated by comparing organic carbon, calculated here, with the one calculated using Thermal Optical Reflectance (TOR) technique. /oH is equal to 11. [OMH] = (1 / /ott) ([H] - 0.25 [S] Salt Assuming all sodium in the aerosol is associated with sea salt as sodium chloride. [NaCl] = 2.5[ Na] Smoke Potassium is a good indicator for smoke from wood burning and bush fires. Its mass is determined from the potassium mass, after removing the potassium associated with soil. [Smoke]=[K]-0.6[Fe] Black Carbon Black carbon or elemental carbon mass concentration 1s measured using the Laser Integrating Plate Method (LIPM). [Elt. C.]= [cra] / E. O'a is the aerosol optical absorption coefficient, and E is the specific mass absorption Chapter3 Aerosol Light Absorption Measurements. 65 coefficient or absorption efficiency. The measuring technique will be discussed in detail in the following section. Reconstructed fine mass The sum of all of the above chemical species should give a fairly accurate estimate of the fine particle mass, compared to the gravimetric mass measured on the filter. Nitrate particles were volatilised from the filter during collection, and are not measured by gravimetric analysis, therefore nitrate is not included in the reconstructed mass. [RCFM] = [sulfate] + [[OMH] +[salt]+ [soil]+ [smoke]+ [EC] (3.28) 3.3.3 Laser Integrating Plate Method (LIPM) Figure 3.6 shows (a) schematic diagram of the Laser Integrating Plate (LIPM) and (b) photo of the system used by ANSTO. Light from He-Ne laser of wavelength 630 nm is diffused and collimated to give a uniform beam, which passes through the loaded filter. The light scattered by particles is angularly integrated by the Lambert scattering opal glass plate, so that the change in light transmission is attributed only to the particles absorption. Aerosol particles are on the surface away from the light source. The transmitted signal intensity is detected using a photodiode detector. Each filter is measured before and after exposure as optical properties of the Teflon filters vary noticeably. For absorption coefficient of aerosol particles measurements, aa can be determined using Lambert-Beer law, Equation (3.13), which can be written as: l I 10 = exp(-aaX) (3.29) lo is the unexposed intensity and l is the exposed transmission intensity, X is the length of the sampled air column, given by dividing the volume of air passed through the filter, V (m3) by the area of the filter exposed A ( cm2). The absorption coefficient then can be obtained by (3.30) Chapter 3 Aerosol Light Absorption Measurements. 66 (a) Opal glass diffuser .·· Photo diode Filter Diffuser Aperture Voltmeter (b) Figure 3.6 (a): Schematic diagram of Laser Integrating Plate System, (LIPM). (b): Photograph of the Laser Integrating Plate System used by ANSTO. Chapter 3 Aerosol Light Absorption Measurements. 67 Huffman (1996a), and Malm et al. (1994) demonstrate the need to correct for excess loading and layering effect. They used an empirical correction factor R to all their Teflon filters in the US IMPROVE network b ap = 0.97 X [ ~ ] (3.31) R = 0.36 x exp[-pt] + 0.64 x exp[-pt] (3.32) 22 415 pt = the thickness I unit area of the particle. On average R == 0.5 for typical aerosol samples, and generally it lies between 0.3 < R < 0.7 for samples collected in ASP network. The 0.97 is a correction factor for large angle scattering. This correction was determined by Campbell et al. (1989), who compared the LIPM measurements with Laser Integrating Sphere Analysis (LISA), which collects back-scattered light as well as the forward-scattered (Huffman, 1996a). The proposed absorption coefficient, after correction is equivalent to (3.33) Malm et al. (1994); Malm et al. (1996); and Huffman (1996b) followed this procedure to estimate black carbon mass concentration from LIPM measurements, using a value of 10 m2t 1 of mass absorption coefficient. ANSTO has adapted the suggested correction and used similar calculation for their black carbon measurements (ERDC, 1995). 3.3.4 Smoke Stain Reflectometer The EEL Smoke Stain Reflectometer photo, used by ANSTO, is shown in Figure 3.7. A diffuse light from a tungsten lamp passes through the orifice of an annular photocell, to project a defined spot on the sample, it is then reflected to the photocell. Reflectance of the exposed filter R (in % ) is compared to the reflectance of a clean one R0 ( 100 % ). The measured light is the light transmitted through the particles plus the light Chapter 3 Aerosol Light Absorption Measurements. 68 scattered by them, thus it is proportional to the absorption coefficient. Light reflected at the surface of the filter has passed through the particle layer on the surface twice, which means that the attenuation caused by the particles, is twice that caused by one pass. Therefore, Equation (3.29) when applied to reflectance measurements becomes (3.34) The relationship between the reflectance and transmittance can be expressed as In lo =0.51n Ro (3.35) I R There is a limitation to this technique when Teflon filters are in use, at reflectance less than 20 % and greater than 90 %, as saturation will occur at higher black carbon concentration. 3.4 Testing methods and measurements Aerosol elemental composition (PM2.5 and PMlO µm) has been measured routinely at ANSTO, (ASP network), using IBA analysis, and LIPM measurements for the black carbon. As part of the ongoing study, tens of thousands of fine particles filters were analysed, and a mass closure study has been carried out by comparing the gravimetric mass with the reconstructed mass (RCFM) in equation (3.28). A comparison over many years and at different sites showed that the RCFM is (87 ± 21) % of the total gravimetric mass (Cohen et al., 2000). The unmeasured species were water vapour and nitrate, which were assumed to account for the missing mass. It was necessary to re-examine the correction procedure, used to convert the black carbon absorption into mass, in order to improve the mass closure between the gravimetric mass and RCFM mass. For the last few years there have been concerns about the accuracy of a0 and the correction factor 1/R used in Equation (3.31 ), since it is only an empirical factor, which lacks theoretical justification. Huffman (1996a; 1996b) used statistical observation of a comparison between (LIPM) and (TOR) carbon measurements to justify the correction, and to explain the inconsistency of previous measurements (Malm et al., 1994) when Chapter 3 Aerosol Light Absorption Measurements. 69 Figure 3.7: Photo of the EEL smoke stain reflectometer, model 43D. Chapter 3 Aerosol Light Absorption Measurements. 70 such comparison resulted in a value 20 m2g- 1 for mass absorption coefficient. He concluded that the measurements are in agreement with each other and with the 10 m2g- 1 absorption efficiency used for the comparison. Horvath (1997b) disagreed with this correction, and suggested a different calibration factor derived by comparing the Integrating Plate technique with the absorption coefficient calculated by subtracting the scattering coefficient measured with a nephelometer from the extinction coefficient of a white cell reference (Horvath and Metzig, 1990; and 1997a). He concluded that a value of 8.52 m2t 1 would be more reasonable. One can easily notice that the differences reported in the literature arise from using different techniques for the standard black carbon measurements which LIPM will be compared to, or using different types of filter substrate for sample collection, or even taking measurements at different wavelengths. Needless to say, the value of the absorption efficiency will also depend on the chemical composition of the measured aerosol particle and its size distribution. Such arguments support the need for a validation process of the LIPM technique used by ANSTO to measure the black carbon mass concentration, and the need to derive a more appropriate value of mass absorption coefficient, which will be more consistent with the technique used, and the type of aerosol measured by the ANSTO network. Results for different types of black carbon samples generated in the laboratory, with a varying ratio of concentration, as well as measurements of ambient aerosol particle samples collected at various sites, are used to derive a more acceptable value of aerosol absorption efficiency. No empirical correction factors will be used, as we think that, at this stage, there is no absolute reference method, which will produce such factor. The derived values were validated with a theoretically calculated one using Mie theory. 3.4.1 Calibration for transmission measurements Black carbon Test aerosol samples were generated in the laboratory using:(1) candle smoke, (2) acetylene smoke and (3) crushed Graphite carbon. The carbon produced was collected using PM2.5 sampler, and deposited on Teflon and Nuclepore filters. (1) and (2) produced soot that comprised - 70 % and -60 % of the total mass, respectively. This Chapter 3 Aerosol Light Absorption Measurements. 71 was similar to the ambient elemental carbon, generated by combustion processes. (3) produced carbon particles that comprised -85 % of the total mass. Detailed average chemical composition of test carbon samples is presented in appendix A. The measured absorption of the carbon, ln[/i/1], varied between O and 6. Total mass of each filter was measured using a gravimetric balance, while the elemental composition was determined using IBA analysis, PIXE, PIGME and PESA. The non-carbon mass was reconstructed using Equation (3.28), without the elemental carbon. The carbon mass was estimated by subtracting the reconstructed non carbon mass from the total gravimetric mass [Est. Elt.C] = [Total Mass] - [Non-Carbon Mass] (3.36) 3.4.1.1 LIPM measurements using black carbon particles Scanning electron microscope (SEM) photographs for representative samples of the three different types of carbon are shown in Figure 3.8 (a, band c). The photos reveal uniform particle loading with an average diameter of - 0 .17 µm for the candle and acetylene carbon, while the size of particles for the graphite carbon was - 5.0 µm. Figure (d) is a blank filter. The ratio between the EC concentration and the LIPM attenuation is related to the absorption efficiency according to the following equation £ = aa = ~ln(/o) (3.37) me m, I A denotes the area of the deposit in cm2, m, is the total deposited mass in µg, and me is the mass concentration in µgcm- 2• Simplifying the above equation then, absorption coefficient of particles becomes Estimated Elt. C was plotted as a function of the absorption coefficient of the black carbon, ln[Ii/1], for all 3 types of black carbon, Figures 3.9, 10, and 11. The correlation between the two values was excellent, with a fairly clear linear dependence. The Chapter 3 Aerosol Light Absorption Measurements. 72 coefficient of least squares fit R2 > 0.97. The derived mass absorption coefficient E of the candle carbon, 1 / slope, was 7 ± 0.5, 6.43 ± 0.4 m2g- 1 for the candle and acetylene carbon respectively. The larger size of graphite carbon yielded a value of 1.5 ± 0.1 m2t1, R2 =0.98. Prediction of the mass absorption coefficient for the candle carbon deposited on a Nuclepore filters, Figure 3.12, gave a slightly higher value of 8 ± 0.7 m2g-1, R2 = 0.96. A possible reason for such behaviour could be due to the coverage of the filter, since the area of the Nuclepore filters is greater than for the Teflon, therefore most of the deposited particles will rest on the filter surface. While it is obvious that different filter media will give different absorption coefficients, more work is required to investigate and explain such differences. 3.4.1.2 LIPM vs. Reflectometer The purpose of this experiment was to test the sensitivity of the LIPM, at lower concentrations, as the reflectometer measurements are not valid at higher concentrations. Reflectance of the candle and acetylene carbon was measured using the EEL Smoke Stain Reflectometer. Figure 3.13 is a scatter plot ofln[Ioll] against ln[R 0 I R] for both the candle and acetylene carbon. Samples of Reflectance R < 20 % and R > 90 % were excluded from the least-squares regression analysis, but were included in the graph. The agreement between the two was excellent and consistent with the theory, as the ratio was. 1n(~)/1n(~) =0.5±0.01 ,R2 =0.99. This result satisfies the assumption that if a light makes double pass through the particles, then the attenuation caused by the particles layer will be doubled. A similar result was obtained using the candle carbon deposited over Nuclepore filters, Figure 3.14. The agreement was excellent and consistent with the theory, as the ratio was 0.49 ± 0.01, (R2 = 0.99). No restrictions were applied, as all samples, including those of R < 20 %, were included since they still obey the linear relationship. Although the graphite carbon gave an excellent correlation between the two measurements, R2 = 0.99, Figure 3.15, the ratio between the two measurements was greater than expected, 0.81 ± 0.01. An obvious reason could be the larger size of the graphite particles. filter filter Teflon Teflon xl0,000 xl0,000 filter, filter, xl0,000, xl0,000, representative representative blank blank for for . . Smoke, Smoke, carbon of of (d):Teflon (d):Teflon photographs photographs Acetylene Acetylene types types : ) b ( (SEM) (SEM) different different three three microscope microscope the the filter filter of of ectron ectron es es l l e xS,000 xS,000 Teflon Teflon samp Scanning Scanning carbon, carbon, 3.8: 3.8: xl0,000, xl0,000, Figure Figure Smoke, Smoke, (cJGrapbitic (cJGrapbitic (a):Candle (a):Candle Chapter 3 Aerosol Light Absorption Measurements. 74 90 80 y = (14.27±0.54)x + (0.26±1.56) c-- E 70 R2 = 0.97 ~ C) 60 :::1. 50 -(.) 40 CD... 30 f/) w 20 10 0 0 2 4 6 ln[IJI] Figure 3.9: Estimated black carbon for the candle experiment using Teflon filters, vs. ln[//1]. Specific mass absorption coefficient e = 1 / slope. 100 N 80 -E (.) C) --:::1. 60 -(.) a:i... 40 f/) w 20 y = (15.55±0.53)x -(0.76±1.1) R2 = 0.97 0 0 2 4 6 8 ln[IJI] Figure 3.10: Estimated black carbon for the acetylene experiment using Teflon filters, vs.ln[//1]. Chapter 3 Aerosol Light Absorption Measurements. 75 400 --.------~ 350 ~ 300 ~ ~ 250 ._ 200 () a:i 150 1n 100 y = (65.9±1.8)x +(3.95±3.8) w 50 R2 = 0.98 0 ~:...,.._...... _...... _...... _4--_.___.___.__....._4--....._..__..__~ 0 2 4 6 ln[lc/1] Figure 3.11: Estimated black carbon for the graphite experiment using Teflon filters, vs.ln[l/1]. 20 --.------~ y = (12.53±0.54)x - (0.39±0.34) N -E 15 R2 = 0.96 ~ C) ::::1. ci 10 a:i ....en 5 w 0.0 0.5 1.0 1.5 ln[lc/1] Figure 3.12: Estimated black carbon for the candle experiment using Nuclepore filters, vs.ln[l/1]. Chapter 3 Aerosol Light Absorption Measurements. 76 7 0 6 y =(0.50±0.01 )x+(0.00±0.01) R2 = 0.99 0 5 Bo !::::'4 0 'o =.. 0oS; c3 0 o'-0 o xR>20% 2 0 coJ) 0 oR<20% 1 c,:, • 0 0 2 4 6 ln[RJR] Figure 3.13: In[/,//] of the LIPM vs. ln[R,/R] of the reflectometer, for candle and acetylene carbon using Teflon filters. 1.5 ~------~ y = (0.49±0.01)x -(0.08±0.01) R2 = 0.99 1.0 ...... 0 ...... C 0.5 0 1 2 3 4 ln[RJR] Figure 3.14: In[///] of the LIPM vs. ln[R,/R] of the reflectometer for candle carbon using Nuclepore filters. Chapter 3 Aerosol Light Absorption Measurements. 77 6 5 x R>20% 0 o R<20% 0 4 ...... y =(0.81±0.02)x - (0.04±0.01) c:f> ~ 3 R2 0.99 cP ...... = 0 C 0 2 0 ~o 1 0 0.0 1.0 2.0 ln[RJR] Figure 3.15: ln[/,/1] of the LIPM vs. ln[R,/R] of the reflectometer, for graphite carbon using Teflon filters. Chapter3 Aerosol Light Absorption Measurements. 78 3.4.2 Theoretical calculation of mass absorption coefficient Mie calculations provide an alternative procedure to the experiment for determining mass absorption coefficient, if enough information about the particles size distribution and its chemical composition are available. For a given refractive index, the absorption efficiency factor Qa can be calculated as a function of x, the size parameter, using (Bohren and Huffman, 1983) Mie code. Assuming spherical particles of radius r, of a material with density p, mass absorption efficiency £ can be calculated as function of r for a given wavelength "A, as shown previously in Equation (3.27). £= 3Qa 4rp 3.4.2.1 Particles refractive index and density calculation The imaginary part of the refractive index determines the damping effect of the light, while the real part corresponds to the velocity of the light wave in the particles. Experimental determination of the refractive index of black carbon is not an easy task, because of the many uncertainties involved in such a process. These uncertainties are the reason for the big differences in the values reported in the literature. Horvath (1993a) gave a selection of refractive indices for elemental carbon: the real part can vary from 1.2 to 2, while the imaginary range is from - 0.1 to - 1.0. In previous attempts to use Mie theory to calculate aerosol absorption coefficient, people either referred to a reported value in the literature (Hitzenberger, 1996a), or assumed certain volume ratio of graphite and air, such as 25:75, 50:50 (Horvath, 1993a). In this work, we have tried to find a more reasonable estimate of refractive index using the measured elemental composition of particles. For the refractive index calculation, additivity for the refractive index is assumed. The average refractive index is obtained using the following (3.38) m;is the refractive index of each species (Table 3.1), and xii Xis the mass fraction of the lh species. Chapter 3 Aerosol Light Absorption Measurements. 79 Table 3.1: Refractive index and density values for different species Species m SULPHATE (NRi)2SO4 1.52 1.77 SALT NaCl 1.54 2.17 SOIL SiO2 1.544 2.65 TiO2 2.56 3.9 AhO3 1.767 4.022 Fe2O3 2.901 5.26 FeO 2.32 5.7 Cao 1.895 2.94 OMH 1.4 1.55 SMOKE 1.44 2.14 Elt Carbon 2-1.0i 2.25 All data from (Lide, 1997) except OMH, which was from (Larson et al., 1988) Chapter 3 Aerosol Light Absorption Measurements. 80 As for the density of BC, p is calculated for a mixture of carbon and air, since BC particles sampled on filters are fluffy particles with space inside (Hess et al., 1998), P = PncXnc where Pnc = 2.25 g I cm3, and xnc is the mass fraction of carbon. Results of calculation of the refractive index and particle density for each type of the black carbon are shown in Table 3.2. 3.4.2.2 Standard black carbon samples Mie calculations of mass absorption efficiency e for the candle and acetylene carbon, at a wavelength A = 630 nm were performed using the calculated refractive index and BC density as in Table 3.2. Figure 3.16 is a plot of e against the particle diameter. The mass absorption coefficient is inversely proportional to the diameter for large size of particles and independent of the diameter for the smaller sizes, it depends very much on the size distribution and the chemical composition of the aerosol considered. At a particle diameter of 0.17 µm (as seen by SEM), the calculated coefficients were e = 7.02 m2i 1 and e = 6.97 m2t 1 for the candle and acetylene carbon respectively for Teflon filters. e = 6.77 m2g- 1 for the candle carbon deposited on Nuclepore filters. The agreement between measured and calculated values is very good for the Teflon filters, while measured e for the Nuclepore filters was higher than the calculated value. This gives further evidence that the difference between the measured values of mass absorption coefficient is because of the effect of the filter media on absorption, rather than the nature of the particles. Table 3.3 summarizes the comparison between the LIPM measurements and calculations for the different types of black carbon particles. 3.4.2.3 Measurements of absorption coefficient of aerosol 3.4.2.3.1 Experimental measurements Ambient aerosol samples are routinely collected on Teflon filters in Mascot area (an inner city site, close to the Sydney international airport), as part of the ASP network, every Sunday and Wednesday. Collected samples, covering 1997, were analysed using ) 3 (cr) 0.56 0.39 0.23 s.d. (g/cm (p) 1.41 1.58 1.56 Elt.C Density (cr) deviation 0.1 0.13 s.d. (m) standard the is Index 0.7 s.d. Imaginary particle. of (cr) Refractive 0.15 0.63 0.25 0.09 0.07 0.69 s.d. density and index 1.86 1.79 1.82 Real refractive Type Carbon Calculated Carbon Carbon filter) filter) 3.2: Carbon Table Candle Candle (Teflon (Teflon Acetylene (Nuclepore filter) Chapter 3 Aerosol Light Absorption Measurements. 82 ~ 'c, N -E .; r:: Q) 1.00 ·u !:E Q) 0 (.) r:: 0 ~e- 0.10 0en ~ -Acetylene Carbon (Teflon) en Candle Carbon (Teflon) en ro ······Candle Carbon (Nudepore) ~ 0.01 ---i--~~---~~---~~---~~---~~---~ ...... 0.0001 0.001 0.01 0.1 1 10 100 Partida diarreter, D (µm) Figure 3.16: Specific mass absorption coefficient E of spherical particles at wavelength A = 630 nm, for the candle and acetylene carbon, deposited on Teflon and Nuclepore filters. Refractive index and density are as in Table 3.2. Chapter 3 Aerosol Light Absorption Measurements. 83 IBA analysis, and LIPM measurements. The mass of black carbon is estimated from the elemental composition of aerosol using Equation (3.28). As already discussed in the previous sections, the mass absorption efficiency of elemental carbon depends on its size distribution and chemical composition. Therefore different sites will produce different types of aerosol and consequently different c coefficients. Even for the same site, the age of aerosol, and the change in source characteristics, will cause a change in the value of c. To estimate an average value of mass absorption efficiency, c is assumed to be constant for a particular site. Figure 3.17 is a plot of estimated elemental carbon vs. absorption coefficient of aerosol particles ln[/oll]. The derived mass absorption coefficient c was 5.9 ± 0.54 m2g- 1, R2 = 0.59. This value is smaller compared to 7, obtained for test carbon. This difference caused by the larger size of ambient particles, its chemistry and consequently, particle density and refractive index. Comparison between weekdays and weekends Filter samples collected on weekdays (Wednesday) showed a different behaviour than the ones collected on weekends (Sunday), (Figure 3.18 and Figure 3.19). The derived value of mass absorption coefficient £for weekdays was 5.8 ± 0.74 m2t 1 with R2 = 0.61, while on weekends£= 4.3 ± 0.92 m2t 1, R2 = 0.37. The difference could be explained by the change of traffic and industrial activities, which will be slower on weekends than on weekdays. The weekend aerosol particles will tend to be larger, as it stays longer in the air before being collected. 3.4.2.3.2 Theoretical calculation Refractive index and particle density of aerosol particles were calculated as described previously. The resultant refractive index m = 1.71-0.38i, with standard deviation a= 0.07 and 0.13 for the real and imaginary parts respectively. The calculated black carbon particle density is p = 0.83 g/cm3, a = 0.3. Calculated refractive value is consistent with other reported values of 1.76-0.45i (Shettle and Table 3.3: Summary of measured and calculated mass absorption coefficient, for the different types of black carbon particles. Carbon Type Particles Diameter e(measured) R2 e (calculated) (µm) (m2g-1) (m2t1) Candle Carbon 0.17 7.01 ± 0.53 0.98 7.02 (Teflon filter) Acetylene Carbon 0.17 6.43 ± 0.44 0.97 6.97 (Teflon filter) Candle Carbon 0.17 7.98 ± 0.69 0.96 6.77 (Nuclepore filter) Chapter 3 Aerosol Light Absorption Measurements. 85 300 250 N -E 200 y = (16.96±1.53)x + (16.45±4.8) (.) R2 = 0.59 -~ - 150 (.) :IK :IK :IK cc_. f/) 100 w 50 0 0 2 4 6 8 ln[lc/1] Figure 3.17: Estimated elemental carbon vs. ln[//J] for Mascot area during 1997. Samples were collected on Wednesdays and Sundays using Teflon filters. Chapter 3 Aerosol Light Absorption Measurements. 86 250 200 N y = (17.2±2.1)x + (13.3±8.6) -E (.) R2 = 0.61 C) 150 -:::1. (.) a:i 100 +.: Cl) w 50 0 0 2 4 6 8 ln[IJI] Figure 3.18: Estimated elemental carbon vs. ln[//1] for Mascot area during 1997. Samples were collected on Wednesdays using Teflon filters. 140 y =(23.2±4. 7)x + (8.82±8.6) 2 120 R =0.37 X N -E 100 X (.) X X ~ -~ 80 X - X (.) a:i 60 +.: Cl) w 40 X X 20 0 0 1 2 3 4 ln[IJI] Figure 3.19: Estimated elemental carbon vs. ln[//1] for Mascot area during 1997. Samples were collected on Sundays using Teflon filters. Chapter 3 Aerosol Light Absorption Measurements. 87 ~ -'c, N E - rust-like(m=1.53-0.008i, derF2.6) - - - v.eter-soluble(m=1.53-0.007i, den=1 .8) ~ ai 1 - rrineral(m=1.53-0.0045i, derF26) ·o • • • Aerosol Masoot (m=1 .71-0 .38i, Den. =0.85) ~ - TraffidMlbile(soo, Yoflter-eoh.ille,OJSt-like) 0 (.) - Residential(soo, Yoflter-sok.tlle) C 0 :;:::; e 0.1 0 (/) .0 <( (/) ea(/) ~ 0.01 0.01 0.10 1.00 10.00 100.00 Partides Diameter, D (µm) Figure 3.20: Modelled specific mass absorption coefficient Eat wavelength A. = 630 run, for aerosol collected at Mascot area, Traffic/Mobile and Residential aerosol models. Mass absorption coefficient for the non-carbon particles, dust-like, water-soluble and mineral are presented too. Chapter 3 Aerosol Light Absorption Measurements. 88 Fenn, 1979), 1.75-0.44i (Jaenicke, 1988), and 1.56-0.47i (Ouimette and Flagan, 1982). Figure 3.20 shows the theoretical calculation of c as a function of particle size. Information about the size of ambient black carbon particles was not available and SEM photographs were inconclusive, as it was impossible to distinguish between carbon and non-carbon particles. The range of c varied between 7.5 to 5.0 m2g- 1 for the size range of 0.17 µm, for the freshly collected primary particles, up to 0.5 µm diameter, which is twice to 3 times the original particle size, with a maximum of 8 m2g- 1 at 0.24 µm. The measured value of 5.9 m2g1 implies particles of average diameter of 0.44 µm, a reasonable size range as most of the particles coagulate. Results of ambient aerosol size distribution, obtained using MFRSR measurements (Chapter 5), shows a fine particle mode of dm = (0.20 - 0.7) µm, which is consistent with the assumed size range for the carbon particles. Black carbon particles are expected to be the main constituent of this fine mode. The agreement between the measured and calculated mass absorption efficiency t: is sufficient, given the many uncertainties involved. Fuller et al. (1999) used more sophisticated models to study the effect of external and internal mixing of particles on optical properties. His calculations included effects of agglomeration, coated carbon dimers and occluded carbon. He concluded that 10 m2t 1 may be over 50 % too high in many cases, and suggested a mass absorption coefficient for light absorbing carbon in diesel soot at wavelength 550 nm may often be less than 7 m2t 1. Therefore a value of c = 6 m2g- 1 for Sydney area would be more appropriate to calculate the black carbon mass concentration, rather than the previously used t: = 10 m2g- 1• Any previous empirical corrections will be discarded. This value of c is in agreement with the one Hitzenberger et al., 1996b, used the study of black carbon data collected in the urban area of Vienna, Austria. A revision of his previous measurement procedure concluded that a value of 6 m2g- 1 which was experimentally determined, is more accurate than the previously used t: = 8 m2g- 1. Chapter 3 Aerosol Light Absorption Measurements. 89 Absorption by non-carbon particles To investigate the absorption by non-carbon particles, mass absorption coefficient E was calculated for two different aerosol models produced by specific sources: Traffic/ Mobile (soot, water-soluble, and dust-like, equally divided), and Residential (soot and water-soluble, 1:2). Power plant / Industrial has similar aerosol component as Traffic/ Mobile (d' Almeida et al. 1983). The test results for the two aerosol models were examined as typical examples. The refractive indices and density of each component are given in Table 3.4. Refractive index and density of soot particle were as derived earlier for Mascot area. Figure 3.20 shows the mass absorption efficiency contribution for each individual aerosol component, and for both aerosol models, the Traffic / Mobile and Residential as a function of particles diameter. It is obvious that contribution of non-absorbing particles is insignificant compared to soot. For particles smaller than 1 µm contribution of soot was more than 97 % for both models. 3.4.2.3.3 Validation A validation process is required to maintain the self-consistency of the aerosol measurements. Such a process is achieved by comparing the reconstructed mass concentration of the aerosol particles with the gravimetric mass. Figure 3.21 is scatter plot of the reconstructed mass against the measured gravimetric mass for Mascot area using Teflon filters collected during 1997. The plot shows a high linear correlation coefficient R2 = 0.91, with a slope of 1.09 ± 0.04. Numerical calculation shows that the average reconstructed mass is 90 % of the original mass, with standard deviation of a= 15%. This value is lower than 100 %, which is expected, because of the water vapour loss during vacuum analysis, and the unmeasured nitrates. Therefore, it is assumed that the missing 10 % of the gravimetric mass composed entirely of nitrates and water vapour. Under the circumstances, the agreement between the reconstructed and gravimetric mass is sufficient. Chapter 3 Aerosol Light Absorption Measurements. 90 Table 3.4: Refractive indices and particles density of various aerosol components at wavelength ,1, = 0.650 µm. Aerosol Refractive index (m) 1 Particle density (p)2 component in g/cm3 Water-soluble 1.53 - 0.007i 2.6 Dust-like 1.53 - 0.008i 1.8 Mineral 1.53 - 0.004i 2.6 Sulfate 1.429 - l.47E-8i 1.7 I. d'Almeida et al. (1983) 2. Hess et al. ( 1998) Chapter 3 Aerosol Light Absorption Measurements. 91 X 30000 C") -E 0) -C -Cl) ~ 20000 E X () ·;:: +-' Q) E ·5 10000 ~ y = (1.09±0.04)x + (265±374) (9 R2 = 0.91 0 -f"-...... __-+-....,__....,__....,__....,__-+-....,__-'---'----'--+---'- ...... 0 10000 20000 30000 Reconstructed Mass mass (ng/m3) Figure 3.21: A plot of gravimetric mass vs. reconstructed mass for Teflon filters collected on Mascot site during 1997. Chapter 3 Aerosol Light Absorption Measurements. 92 3.4.2.3.4 LIPM vs. Reflectometer Measurement from various sites of ASP network were analysed using LIPM and Reflectometer. Figure 3.22 is a scatter plot of absorption coefficient ln[/c/1], against the measured ln[Rc/R] of Teflon filters collected during May, July August and September 1998. Discarded measurements (R < 20% and R > 90%) are also included. The linear regression analysis yielded the ratio of In(~)/1n( ;) = 0.58 ± 0.01, R2 = 0.92. However, if samples of R < 40% were discarded as Petzold and Niessner (1995) suggested, the ratio becomes 0.53, with R2 = 0.81. Similar measurements of Nuclepore filter, yielded the ratio ln( ~) / 1n( ;) = 0.45 ± 0.01, R2 = 0.96. None of the measurements were rejected. Although in this case ratios for the two filter media differed by 16 % and - 10 % from the theoretical prediction, the difference between the two is noticeable, and highlights the effect of filter medium on both the transmittance and reflectance processes. However, since no such differences were observed, when the comparison were applied to acetylene and candle carbon, we believe that the reason for such difference is the variance in particle morphology and size distribution, as differences were noticed too, when it was applied to the large particles of graphite carbon. Fuller et al. (1999) performed extensive Mie calculations on the effect of different scenarios of external and internal mixing of aerosol particles, and reported in some internally mixed aerosol particles, absorption could be enhanced to up to 30 % 3.4.2.3.5 Possible overestimation It was generally considered that the integrating plate technique overestimates the particles absorption coefficient. Clark (1982a) considered internal reflectance of Chapter 3 Aerosol Light Absorption Measurements. 93 7 90%>R>20% 0 6 o R<20% and >90% 0 5 y =(0.58±0.01)x + (0.19±0.01) 0 ...... 4 R2 =0.92 0 'b9c '"'-a ;::::. C: 3 2 1 0 0 1 2 3 4 ln[RJR] Figure 3.22: A plot ofln[//1] of LIPM vs. ln[R/R] of reflectometer, ofambient aerosol for all sites in the ASP network, collected using Teflon filters on May, July, Aug. and Sept. 1998. 0.8 ~------~ 0.7 y = (0.45±0.01)x + (0.00±0.0) 0.6 R2=0.00 • 0.5 ...... ~0.4 C: 0.3 0.2 0.1 0 --""-'-"-'-.L.....,1.-'--'--L...... I..--I-.L.....j.-'--'-L...... L._,_...... _.___,....__.__J...... L-l....-1-,JL...... L.--I-.J...... i 0 0.2 0.4 0.6 0.8 1 1.2 ln[RJRJ Figure 3.23: A plot ofln[//1] ofLIPM vs. ln[R/R] ofreflectometer, of ambient aerosol for selected sites in the ASP network, collected using Nuclepore filters on July and Aug. 1998. Chapter 3 Aerosol Light Absorption Measurements. 94 light through the filter is the reason for any increase in absorption. A number of authors (Hitzenberger, 1993; Edwards et al., 1983; Lin et al., 1973; Clark, 1982b; and Horvath, 1993b) have reported variations of absorption coefficient of aerosol of 20 - 30 %, when Nuclepore substrate was used. However, (Campbell et al., 1995) has published a calibration study of LIPM, using an integrating sphere LISA. He demonstrated that the method is free from significant overestimation errors, when aerosol samples are collected on Teflon filters, if the appropriate correction is applied when filter loading exceeds a monolayer of particles. He concluded that Teflon membrane does not show the internal surface-to-surface reflections postulated by Clark (1982a) for Nuclepore substrate. The work done here does not demonstrate an overestimation of absorption coefficient measured by LIPM. The consistency between the experimentally and the theoretically determined value of mass absorption coefficient put more confidence on this result. However, one should not rule out any overestimation, whether it was because of multiple scattering or shadowing. Unless someone comes up with the absolute reference method, the accuracy of carbon absorption measurements will always be disputed. As a direct result of the work presented here, ANS TO correction procedures for the LIPM black carbon measurements for the last 5 years, have been revised, and it was concluded that a value of 7 m2g"1, for the entire ASP network, would be more appropriate. All empirical corrections previously used, were discarded too. Our estimated value of 6 m2g" 1 was only derived for one site, Mascot, using the 1997 measurements. 3.5 Summary and conclusion Calibrations for Laser Integrating Plate Method have been performed on Teflon and Nuclepore filter media, using different types of test black carbon, in order to determine the specific mass absorption coefficient or efficiency. It was found that the value of 10 m2g·1 previously used for mass determination, and widely accepted for diesel fuel emission, is an inappropriate choice. For the acetylene and Chapter 3 Aerosol Light Absorption Measurements. 95 candle carbon E was 7 m2g- 1, while for ambient aerosol observed in Sydney, E was closer to 6 m2g- 1• The experimentally determined mass absorption coefficient was in good agreement with the theoretically determined value. The reported overestimation of absorption coefficient was not noticed here, when measurements of LIPM were compared against the Reflectometer. However, such overestimation, if existed, is not expected to impact on mass determination, as the mass absorption coefficient was empirically determined using the same measuring technique. IBA analysis provided valuable information of the ambient aerosol particles, and its elemental composition. PIXE, PIGME, and PISA measurements were used to predict the particles refractive index and density, while SEM electron microscope images were used to determine the soot particle size. The calculated refractive index was m = (1.71 ± 0.07) - (0.38 ± 0.13)i, and a density of p = (0.83 ± 0.3) g/cm3• These critical parameters were required for more accurate theoretical Mie calculation to determine the mass absorption efficiency. Such key ambient black carbon parameters were previously unknown for Sydney type of aerosol, and people had to rely on values reported in the literature. For the future, more work is required to analyse the effect of different filter media on absorption measurements, as this part of the work was incomplete. Unfortunately, PIGME analysis, which is critical in determining the organic matter, was not available for Nuclepore filters. More information about the morphology of ambient aerosol particles and its size distribution would lead to a better understanding of variations in absorption by atmospheric carbon particles. Future work would also involve combining the nephelometer and LIPM measurements to obtain an estimate of the single scattering albedo m, and comparing the result with the value obtained using the MFRSR diffuse to direct measurements (Chapter 6). This requires more frequent samples of smaller periods (3 - 6 hour), rather than the (24 hour) used here. Chapter 4 The Use Of A Thermodynamic Model In Predicting Aerosol Growth Factor 4.1 Introduction A substantial amount of research work has been reported on the composition of atmospheric aerosols as a function of relative humidity (Pilinis et al., 1989; Hegg, 1993; Kim et al., 1993; Pitchford and McMurry, 1994; Hitzenberger et al., 1997; Neubauer et al., 1998; Ohta et al., 1998; and Zhang et al., 2000). Atmospheric aerosols generally consist of solid and/or liquid multi-component particles that can include inorganic (sulfate, nitrate, ammonium, sodium, and chloride), and organic compounds, elemental carbon (soot), soil and a variety of trace metals. Inorganic species comprise up to 50% of atmospheric fine aerosol mass, and the fraction could be higher if water was included (Potukuchi and Wexler, 1995). Depending on the relative humidity, these inorganic species may be present as multi-component solutions of the electrolytes or as pure solid phases (Wexler and Seinfeld, 1991). For example, an aerosol particle consisting of sodium, ammonium, chloride and nitrate may be composed of an aqueous solution of Na+, NH/, er, and NO3- and pure solid phases of sodium chloride, ammonium chloride, sodium nitrate and ammonium nitrate. Inorganic salt aerosols are mostly hygroscopic by nature and exhibit the property of deliquescence in humid air. The phase transformation from a solid particle to a saline droplet usually occurs spontaneously, when the relative humidity (RH) in the surrounding atmosphere reaches a level, known as deliquescence humidity (DRH), that is specific to the chemical composition of the aerosol particle (Tang and Munkelwitz, 1994). It is generally assumed that volatile species in the gas (air) and aerosol phases are in chemical equilibrium. An equilibrium of atmospheric aerosols is an essential tool in analysing ambient aerosol data, and in productive modelling of atmospheric aerosols. Such a model can calculate important equilibrium properties that are Chapter4 The Use of Thermodynamic Model ... 97 difficult to measure, like the water content and acidity of the particles. Water content is critical to estimating particle mass, size, and optical properties. Models include EQUIL (Bassett and Seinfeld, 1983), MARS (Saxena et al., 1986), SEQUILIB (Pilinis and Seinfeld, 1987), AIM (Wexler and Seinfeld, 1991), SCAPE (Kim et al., 1993a), SCAPE2 (Meng et al., 1995), and EQUISOL V (Jacobson et al., 1996). In this chapter, we will investigate the use of the equilibrium model for inorganic multi-component atmospheric aerosol, SCAPE2 (Kim et al., 1993a and 1993b; Meng et al., 1995). The model assumes a system of ammonium/ chloride/ nitrate / sodium / sulfate / water which exists in solid, liquid and gaseous phase. Given the concentration of the respective elements for a mixture of water-soluble inorganic particles, as well as RH and temperature, as an input, the model calculates the equilibrium physical state and composition of the mixture. The aerosol elemental composition, measured by IBA analysis, is the input of the model. Water and inorganic species calculated by the model, alongside other species ( organic, soil, and elemental carbon), are used to predict the enhancement growth factor. Both molar fraction and mass fraction approaches were used to calculate refractive index and density of particles. And empirical models were used to determine values of these parameters as a function of relative humidity. 4.2 Experimental measurements Fine particles mass measurements at Mascot Fine particulate measurements, PM2.5 µm, were measured routinely by ANSTO, using the PM2.5 sampler, described in Chapter 3, at Mascot, 5 km away from the MFRSR location. Average fine particle mass concentration for Mascot was 10 µgm- 3 for 1997. Inorganic species comprises 30 % of total mass, with standard deviation CJ= 15 %, organic species were 23.4 %, CJ= 15.4 %, elemental carbon was 27.6 %, CJ= 12.9 %, 5 % of the mass was fine windblown soil, CJ = 2.1 %, and Trace elements (including Pb, Br, Zn, and Mn) were 1.3 % of total mass, CJ= 0.7 %. Chapter4 The Use of Thermodynamic Model ... 98 4.3 Thermodynamic equilibrium model 4.3.1 Theory of equilibrium model The thermodynamic model for Simulating Composition of Atmospheric Particles at Equilibrium (SCAPE2) considers an aqueous particle containing a mixture of electrolytes, ammonium / chloride / nitrate I sodium I sulfate and water. Assuming that this system is in water equilibrium, the relative humidity (RH) of the atmosphere can be taken as the water activity of the solution by neglecting the Kelvin effect, since most of the particulate mass is associated with particles > O.Olµm diameter. The aerosol population is assumed to be an internal mixture of all species involved. At high humidity, all species will be dissolved in the aqueous phase and no solid salt phase will exist. As the RH is reduced, one of the solid phases may form a salt precipitate, which is in equilibrium with its ions in the solution. At still lower RH, a second solid phase may be formed and, if we continue reducing the RH, we reach a point known as the mutual deliquescence relative humidity (MDRH), below which the aqueous phase disappears and all compounds exist as solid crystals (Potukuchi and Wexler, 1995). 4.3.2 Chemical potentials and equilibrium constants The condition for chemical equilibrium in a closed system, such as the one we are considering, at constant temperature T and pressure p, is that the total Gibbs free energy of the system, G(T,p,nJ, is a minimum or equivalently: L v iJ µ i = 0 , for all reactions j. (4.1) where ni is the concentration (moles m-3 of air), ViJ is the stoichiometric coefficient of the /h species in the /h reaction, and µ; = (:) T, p, n c is the chemical potential of species i, which is defined as A= µ;°(T)+ RTlna;. (4.2) where µ,;°(T) is the standard chemical potential for the /h species at 1 atm and temperature T (in K), R is the gas constant and ai is the activity of the /h species. For a pure solid phase ai = 1, for an ideal gas ai = Pi where Pi is the partial pressure of the {h Chapter 4 The Use of Thermodynamic Model... 99 species (in atmosphere), and for all aqueous solutions of electrolytes (Denbigh, 1981) (4.3) where ')1 is the activity coefficient of species i in water. V+ and v_ are the moles of the electrolyte, v = v_ + V+ is the total number of ions. y ± is the mean ion activity coefficient, and it can be calculated by (Robinson and Stokes, 1959) (4.4) and m+ and m_ are the molalities of cations and anions (mol solute/kg solvent) respectively, Where (4.5) m_ = y_m (4.6) m ± is the mean ionic molality, given by m = (mv· mv_ )¼ ± + - ' (4.7) and m is the total molality (4.8) substituting Equation (4.2) into (4.1) (4.9) or K J- = IT a~•1I ' (4.10) where l(_j(T) is the equilibrium constant of thejth reaction, (4.11) The standard chemical potential for temperature T can be calculated from: Chapter4 The Use of Thermodynamic Model ... JOO 0 0 O /)..G M-f° /)..C ( T) L=--1 +--1 (T-2__1 ) +--p 1+1n (T)-2_ _-2_ (4.12) RT R~ R~ T R T T ' 0 where T0 is the standard-state temperature (298.15 K) and ll.G/, !::i.Hf, and Cp are the standard molar Gibbs free energy of formation, the standard molar enthalpy of formation, the standard molar heat capacity at constant pressure, respectively. !::i.Hf, and C/ are constant over the temperature range (Tto T0 ). Combining Equations (4.10) and (4.11) gives: (4.13) Equilibrium relations and constants for the reactions involved in the model at specific temperature can be obtained from Table 4.1. 4.3.3Activity coefficients Many different approaches that predict the activity coefficients of electrolytes in multi-component solution use the activity coefficient of binary mixtures of the same ionic strength for their predictions. Kim et al. (1993a) compared various models for predicting the activity coefficient in multi-component solutions. These models included: (K-M method (Kusik and Meissner, 1978), Bromley method (Bromley, 1973); Polynomial Regression method (Basset and Seinfeld, 1983), and Pitzer method (Pitzer, 1986). He concluded that Pitzer's method performs better for the low concentration data that they considered. SCAPE2 has the ability to perform calculations using K-M, Bromley, and Pitzer methods. Nonetheless, the Pitzer method would be the preferred method for this work, due to its rigorous thermodynamic treatment. Pitzer calculations are carried out as the following (4.14) Chapter 4 The Use of Thermodynamic Model ... 101 Table 4.1: Equilibrium relations and constants. EquilibriWD constant• Equilibrium relation Constant expression K(298.15) a b Unit [ff+ )[SO}-JyH+ 'Ysol- HSO; NH3(&q) + ff 20Caq)- NH;(aq) [NH; J[off-hNw"Yow +OW(aq) 1.805 X 10-s -1.50 26.92 molfkg [NH3(aq)hNH,a., [WJ(NOjhwl'No;- HNO3(g) - ff+ (aq) + NOj(aq) 2.511 X 10611 29.17 16.83 mol1/kg 1 11m PHNo, [ff+ 1(0-]yH•l'o- HO(g)- W(aq) + a-caq) 1.971 X 106 30.20 19.91 mol1/kg 1 atm pHCI [W )[oH-hw'Yow H 20Caq) • u•caq) + oH-(aq) 1.010 X 10- 14 -22.52 26.92 mol1/1cg1 a., Na2SO4(s) • 2Na•(aq) + so;-(aq) [Na•121so;-h~.·Ysoi- 4.799 X 10-I 0.98 39.75 mol3/kg 3 (NH 4 )1SO4(s) • 2Nff: (aq) +so;-Caq> [NH;J2[S0;-J-y~H;'Ysoj- 1.817 -2.65 38.57 mol3/kg3 1 1 NH 40Cs) - Nff3(g) + HCl(g) PNH,Po 1.086x 10- • -71.00 2.40 atm NaNO3(s) • Na •(aq) + NOj (aq) [Na•J[NOihN.·l'No;- 11.971 -8.22 16.01 mol1/k, · NaCKs) • Na•(aq) + a-(aq) [Na•J[a-Jy,._ • .,.0 - 37.661 -1.56 16.90 mol1/kg: NaHSO4(s) • Na•(aq) + HSO;(aq) [Na+J[HSO,-J-y"'• l'Hso; 2.413 X 104 0.79 14. 746 mol1 /kg1 NH,NO3Cs}• NH 3(g) + HNO3(g) PNH,PHNo, 5.746 X 10- JlC -74.38 6.12 atml NH 4 HSO,(s) • NH;(aq) +HSO4(aq) [NH,][HSO,-J-y.,Ht'YHSO; 1.383 X 10' -2.87 15.83 mol2/kg2 (NH4 )3H(SO,)i(s) - 3NH; (aq) [NH; )3[HSO,- ][SO;-J + HSO,-(aq) + so,-(aq) l'~Ht'YHso;'Yso.; 29.72 -S.19 54.40 mol 5/kg 5 •eom.tants • and b arc in K•K(T.>H i- 1) +b( I+ ln(i )-i )]. where T0 • 298 K. Source: (Kim et al., 1993). Chapter4 The Use of Thermodynamic Model ... 102 m is the molality of that electrolyte for the given ionic strength, and where jY=-0.392[ /½I +~m(l+b/½)], (4.15) 1+ bf½ b (4.16)) r - 3 e12 - - c~12• (4.17) 2 b = 1.2, a= 2.0, and I is the ionic strength, (4.18) z; is the absolute number of unit charges on ion species i, /312<0 ), /31i°) and C12< 4.3.4 Water activity Since water is by far the most plentiful gaseous species of those involved in the equilibrium, the change in the vapour pressure of water from that in the ambient air will be negligible. Thus, the relative humidity can be treated as constant and known for calculation (Basset and Seinfeld, 1983), and by neglecting the Kelvin effect, water activity aw in the aqueous phase is found to be RH, that is aw = RH , (0 ::; RH ::; 1). (4.19) Water activity is used in the model to calculate the water content of the aerosol using Zdanovskii-Stokes-Robinson (ZSR) method (Stokes and Robinson, 1966). It can be expressed as (4.20) Chapter 4 The Use of Thermodynamic Model ... 103 Table 4.2: Pitzer method binary activity coefficients. Species c 0.0068 0.1783 -0.00072 upto6 NaCl 0.1082 0.03127 -0.00247 0.1-13.6 0.0861 0.13027 -0.0031 0.1-12.8 0.04763 0.44459 -0.00131 0.lx17.9 -0.0154 0.112 -0.00003 up to 6 N~Cl 0.04568 0.20431 -0.00173 0.1-23.2 -0.09330 0.3281 0.021162 0.25713 0.35308 -0.00283 0.04494 0.23594 -0.00292 0.1119 0,3206 0.0010 upto 3 HCI 0.1775 0.294 0.00080 upto6 NaHS04 0.35262 1.56403 -0.009978 Source: (Kim et al., 1993). Chapter4 The Use of Thermodynamic Model ... 104 where m; is the molality of species i in a multi-component solution and m;0 (aw) is the molality of the binary solution at the desired water activity aw of the solution. m; = M; I W, where M; is the molar concentration of species i in the air (mol /m3 air) and W is the mass concentration of water in aerosol (kg water/ m3 air). From the previous relations, water content of aerosol is (4.21) Electrolyte molalities over a range of aw for species of atmospheric interest are available in Cohen et al. (1987), Chan et al. (1992), and Tang and Munkelwitz (1994) 4.4 Application of the model 4.4.1 The NaCl, NH3, O2SO4, HNO3,H2O system The atmospheric system consists of the following potential components: Gas phase : NH3, HCl, HN03, H20. Liquid phase : H20, NH/, S04-2, N03-, H+, Na\ er, HS04- and H2S04. Solid phase : Na2S04, NaHS04, NaCl, NaN03, N~Cl, N~N03, (NH4)2S04, N~HS04, and (N~)3(S04)2. The molar ratio of ammonium and sodium to sulfate and relative humidity are the most important variables in determining the composition of each phase. Another important consideration is the relative humidity of deliquescence. Since the sulfate and sodium compounds have a very low vapour pressure at ambient RH, it can be assumed to occur completely in the aerosol phase. Aqueous or solid Na2S04 and (N~)iS04 are preferred forms of sulfate, and when both ammonium and sodium exist exclusively in the aerosol phase, sulfate is assumed to combine preferentially with sodium. If there is sufficient ammonium present, each mole of sulfate will remove 2 mole of ammonia from the gas phase. If there is insufficient sodium and ammonium in the aerosol phase relative to the sulfate presence, then other forms of sodium sulfate such as NaHS04, and ammonium sulfate, such as NH4HS04 or (NH4)3(S04)2, can be formed.(Kim et al., 1993). Thus the molar ratio of positive ion components (ammonium and sodium) to sulfate is of Chapter4 The Use of Thermodynamic Model ... 105 fundamental importance in determining the equilibrium state. Let [NH/]T, [Na+]Tand [SO4-2]T be the total concentration of ammonium, sodium and sulfate in the gas and aerosol phases. Then the ratio Rs is (4.22) If Rs > 2, the system is sulfate poor, the sulfate rich case can be divided into two subcases. When 1 ~ Rs < 2 part of the sulfate is neutralized, while the rest of it reacts to produce HSO4-. For Rs < 1, the solution is very acidic; part of the H2SO4 dissociates to HSO4-, while the rest remains as H2SO4u) (Pilinis and Seinfeld, 1987). For a relative humidity above DRH of a specific solid, the solid may not exist, and, if the RH is below the DRH, the solid phase may or may not exist, depending on the thermodynamic equilibrium. Therefore, the equilibrium between solid and ions in aqueous solution for a specific salt must be considered in order to examine whether any solid exists. For RH lower than the lowest DRH of the salts involved, the aerosol is assumed to be dry. 4.4.2 Computational algorithm The first step is to divide the entire concentration domain into sub-domains according RH, which will determine possible solid components at equilibrium in the aerosol depending on DRH, and the ratio R8• Table 4.3 gives relative humidity of deliquescence for nine possible solid compounds, while Table 4.4 and Table 4.5 are the negligible and non-negligible solid species for the system The second step is to calculate an initial guess of concentration from the measured concentration, which will be the starting point for iterative solution. The model will be solving a set of non linear Equations (4.10) together with the mass conservation, and electro-neutrality constraints. Water content is used as the initial guess for subsequent iterative calculation of the full equilibrium concentration of gas and liquid phases. This step is iterated until solid concentration converges within a preset error (Kim et al., 1993b). Figure 4.1 is a schematic diagram of the algorithm used in this model. Chapter4 The Use of Thermodynamic Model ... 106 Table 4.3: Relative humidity of deliquescence for nine possible solid compounds. Species Deliquescence RH NaCl 76.0 93.0 NaHSO4 52.0 74.0 N~Cl 80.0 80.0 69.0 40.0 62.0 Source: (Pilinis and Seinfeld, 1987). Chapter 4 The Use of Thermodynamic Model ... 107 Table 4.4: Aerosol chemical composition as a function of the ambient relative humidity for the sulfate deficient case. RH Species 100 93 Na2S04(s), H20 N&+, S04-2, N03-, Na+, er 80 Na2S04(s), N&el(s), (N&)2S04(s), H20 N&+. S04-2, N03-, Na+, er 76 Na2S04(s), N&el Source: (Pilinis and Seinfeld, 1987). Chapter 4 The Use of Thermodynamic Model ... 108 Table 4.5: Aerosol chemical composition as a function of the ambient relative humidity for the sulfate rich case. RH 100 NH.i +, S04-2, N03-, Na+, er H\ HN03(1), HS04-, H20 93 Na2S04(s), H20 NH.i +, S04-2, N03-, Na+, er H\ HNOJ(l), HS04-, H20 80 Na2S04(s), (Nl!i)2S04(s), H20 Na+, er, NH.i+, N03- NH.i\ S0/2, N03-, Na+, er H\ HN03(1), HS04 H+, HN03(1), HS04- H2S04(1), H20 69 Na2S04(s), (Nl!i)3H(S04)2(s), (Nl!i)2S04(s) NH.i+, S04-2, N03-, Na\ er H\ HN03(1), HS04-, H20 52 Na2S04(s),(Nl!i)3H(S04)2(s), (Nl!i)2S04(s) NaHS04(s), H20 NaHS04(s), H20 Na+, er, NH.i\ N03- NH.i\ S04-2, N03-, Na+, er H+, HN030), HS04- H\ HN03(1), HS04- H2S04(1) 40 Na2S04(s), (NI!i)JH(S04)2(s), (Nl!i)2S04(s) NaHS04(s), H20, Na+, er NaHS04(s), NH.iHS04(s) NH.i +, N03-, HS04- NH.iHS04(s), H\ HN03(1) H2S04(1) 0 Source: (Pilinis and Seinfeld, 1987). Chapter 4 The Use of Thermodynamic Model ... 109 measured concentration determine subdomain calculate initial solid concentration solve nonlinear equations [ no conver ed calculate solid and liquid species converged ,---''--, End Figure 4.1: Simple schematic diagram of the algorithm used in the system. Chapter4 The Use of Thermodynamic Model ... 110 Input values The model considers the chemical system of sodium (Na+), sulfate (H2SO4), ammonia (NH3), nitrate (HNO3), chloride (HCl), potassium (K+), calcium (Ca++), magnesium (Mg+l, carbonates (H2CO3), and water, all in µgm-3. Input values will include temperature in K, and relative humidity. To calculate each species concentration from the elemental measurement of PIXE analysis, a similar procedure to that described earlier is used (see Chapter 3, section 3.3.2.2). The main aerosol species will be calculated as follows: [H2SO4] = 3 [S] [NH3] = 1.28 [N] [HNO3] = 4.5 [N] [HCl] = 1.03 [Cl] Nitrate particles were volatilised from the filter during collection. However, ion chromatography analysis around Sydney indicated that average nitrogen concentration is 2.2 % of the total fine mass (ERDC, 1995). Therefore, nitrogen concentration will be estimated as 2.2 % of the total gravimetric mass. Na+, K+, and Ca++, are directly measured. It was assumed that aerosol carbonate change does not affect the CO2 level in the atmosphere. Carbonate concentration is calculated using the CO2 global average concentration of 360 ppm (Meng, et al., 1995). [H2CO3] = 0.9964 µgm-3. 4.5 Refractive index and molar fraction Prediction of mean real refractive index of urban aerosol The aerosols mean real refractive index is estimated using the aerosol chemical composition, and partial molar refraction (Stelson, 1990). The molar refraction R, of a condensed phase can be expressed as Ti n 2 R=rln2+2-1) (4.23) n is the real refractive index and Vis the molar volume in cm3 mor1 Chapter 4 The Use of Thermodynamic Model ... 111 V= Lx;M; (4.24) p where x; is the mole fraction of species i, M; is the molecular weight of species i in g mor1 and p is the density of the condensed phase in g cm-3• The molar refraction of a mixture can be calculated from partial molar refraction R; using (4.25) The mean refractive index of a medium, n, is calculated from n=[l+23/vJ½, 1-3/v (4.26) and for an aerosol, (4.27) V [AV] R; is the partial molar refraction of component i in cm3 mor1, M; is the molecular mass of component i in g mor1, [S;] is the concentration of component i in µg m-3, and [AV] is the aerosol volume in µm3 cm-3. The mean aerosol density can be calculated using (4.28) The partial molar refraction for an electrolyte is assumed to be the same as for an ionic solid as (Stelson, 1990) suggested. An alternative to the molar fraction approach, for calculating the mean refractive index and density of aerosol particles, is to calculate the contribution of each species using its mass fraction (see Chapter 3, section 3.4.2.1). Both methods were used in this work and the results will be discussed in the following section. Chapter4 The Use of Thermodynamic Model... 112 4.6 Results and discussion The main output inorganic species from SCAPE2 for the Mascot area, using the yearly average elemental composition, were: NaCl, Na2S04, (NRi)zS04, H2S04, HN03, HCl, K2S04, CaS04, MgS04, H2C03 and water. The predicted inorganic species, alongside the organics, elemental carbon and soil, will be used to predict a growth factor, refractive index, and density of aerosol particles as a function of relative humidity. 4.6.1 Prediction of aerosol particle growth The water uptake of airborne particles was calculated using SCAPE2 thermodynamic model at a constant temperature, T = 298 K. It was assumed that growth is exclusively due to inorganic species. A growth factor was calculated as the ratio of total mass of the deposited aerosol, at a particular RH, and its dry mass, f(RH) = m I m0 • Data were fitted into several empirical models, and the following is the one that produced the best fit. f(RH) = m I m0 = a(l - RH)-r (4.29) RH, the relative humidity, a and y are the parameters of this model. Since the chemical composition of the aerosol particles varied during the year, m I m0 was calculated for each month, as well as for the whole year. This will enable us to calculate an effective growth factor f(RH), which can be used on a monthly basis for an accurate humidity corrections. Growth factor was the highest during winter, (f(0.95) == 2.6), and the lowest during summer, (f(0.95) == 1.6). The yearly average growth factor was f(0.95) == 1.9. Table 4.6 is summary of the obtained fitting parameters, a, y, the least squares fit coefficient, R2, and the average mass concentration in (µg/m 3). Figure 4.2 is a plot of the yearly average growth factor f(RH) for Sydney area as a function of relative humidity (RH). Hitzenberger et al. ( 1997) performed a comparison between the measured and calculated m I m0 , derived using SCAPE 1 and found that the predicted value agreed well with the measured data, despite the limited information on the chemical composition of the deposited aerosol. This reported result gave us confidence in the use of SCAPE2, as an alternative method to the experiment, to predict an effective ambient aerosol growth factor, despite the lack of information about exact nitrate concentration. Chapter4 The Use of Thermodynamic Model ... 113 Table 4.6: Average particle mass and fitting parameters used in Equation (4.29), obtained for monthly, and yearly averages for the year of 1997 Month Mass (µg/m 3) a y JAN 5.18 0.51 0.57 1.00 FEB 5.16 0.71 0.45 0.99 MAR 7.13 0.52 0.48 0.99 APR 12.16 0.90 0.26 0.98 MAY 8.83 0.90 0.21 0.98 JUN 13.34 0.90 0.15 0.96 JUL 11.92 0.89 0.18 0.97 AUG 12.10 0.91 0.17 0.97 SEP 9.29 0.90 0.24 0.98 OCT 11.34 0.57 0.38 0.99 NOV 10.01 0.85 0.34 0.99 DEC 9.96 0.51 0.52 0.99 YEAR 9.70 0.84 0.26 0.98 Chapter4 The Use of Thermodynamic Model ... 114 4.6.2 Prediction of real refractive index and density of ambient aerosol Calculations of the mean real refractive index and particle density were carried out as described in section 4.5. Table 4.7 gives the main species density and refractive index. Information about other minor species can be found in the list of references. The mean real refractive index varied from 1.73 at RH= 0.3 to 1.51 at RH= 0.9. Figure 4.3 is a plot of the mean real refractive index of aerosols in Sydney as a function of relative humidity. Similar results were obtained using the mass fraction approach. The predicted real refractive index as a function of relative humidity was fitted into empirical model similar to (4.29) n = 1.78(1-RH)°-07 , R2 = 0.99 (4.30) n is the real refractive index of ambient aerosol. The resultant refractive index is slightly higher than some of the reported refractive index values, 1.47 ± 0.05 for Los Angeles Basin, by (Stelson, 1990), and 1.5 by (Esnor et al., 1972), while for Calgary, Canada, the reported refractive index was 1.53 ± 0.05 by (Mathai and Harrison, 1980). The reason for that could be due to the effect of elemental carbon, which comprises 30 % of the total mass. Calculation of particle density indicates that significant water absorption by particles will cause the density to decrease, since the density of water is lower than the density of the particles. Density of aerosol particles was calculated, using the yearly average chemical composition. Results of the calculation showed that particle density using the molar fraction approach underestimated the density calculated using mass fraction approach by - 0.2 g/cm3. Figure 4.4 and Figure 4.5 are plots of the aerosol density, p in (g/cm3), as a function of relative humidity (RH), using both molar and mass fraction approaches, respectively. The molar fraction method yielded a density range of ambient aerosol between 1.9 g/cm3 at RH= 0.3, and 1.44 g/cm3 at RH= 0.9. While the mass fraction approach yielded a value between 2.11 g/cm3 for RH = 0.3, and 1.72 g/cm3 at RH= 0.9. Results of the calculated density were fitted into similar model to (4.29) p = 2.03(1- RH)°- 15 , R2 = 0.99 (4.31) for the molar fraction method, while mass fraction method gave the following fit Chapter4 The Use of Thermodynamic Model ... 115 2 ~ ~ 1.9 'E 1.8 ~(I) u. 1.7 .s:::. j 1.6 e (9 1.5 Q) 0 1.4 :e (I) 1.3 a. 0 1.2 U) 1.1 eQ) <( 1 0 0.5 Relative Humidity RH Figure 4.2: Growth factorf(RH) plotted against relative humidity (RH), for Sydney area. 1.75 X 1.70 Q) "C 1.65 -=Q) :p> 0 (0 1.60 ~ Q) 0::: 1.55 "iij Q) 0::: 1.50 1.45 0 0.5 1 Relative Humidity Figure 4.3: Mean real refractive index of ambient aerosol for Sydney area plotted against relative humidity (RH). Chapter4 The Use of Thermodynamic Model ... 116 '7 2.2 -E (.) -9 2.0 ·u;~ C: 1.8 Q) 0 Q) 1.6 c3 :eco 1.4 a.. 0 1/) 1.2 ...0 Q) Figure 4.4: Mean aerosol particle density, p (g cm-3), calculated using molar fraction approach, plotted against relative humidity (RH), for Sydney area. '7 2.2 -E (.) Cl 2.0 -~ ·u; C: 1.8 Q) 0 Q) 1.6 c3 :eco 1.4 a.. 0 1/) 1.2 eQ) Figure 4.5: Mean aerosol particle density, p(g cm·3), calculated using mass fraction approach, plotted against relative humidity (RH), for Sydney area. Chapter4 The Use of Thermodynamic Model ... 117 Table 4.7: Density and refractive index of the main aerosol particle species. Species Density p (g/cm3) Refractive Index Reference Water 0.997 1.33 1 NaCl 2.17 1.544 2 Na2SO4 2.7 1.475 1 (NH4)2SO4 1.77 1.523 1 H2SO4 1.841 1.419 2 HNO3 1.67 1.47289 2 HCl 1.19 1.53 3 K2SO4 2.66 1.494 2 CaSO4 2.96 1.575 1 MgSO4 2.66 1.455 1 H2CO3 2.3 1.6 3 Organics 1.4 1.55 3 Elemental Carbon. 2.25 2.0-1.0i 1 Soil 3.9 2.03 4 I (Lide, 1997) 2 (Washburn, 1926) 3 (Larson, et al., 1988) 4 Calculated using mass fraction contribution, as described in chapter 3. Chapter4 The Use of Thermodynamic Model ... 118 p = 2.24(1- RH) 0.1 1 , R2 = 0.99 (4.32) The resultant particle density of ambient aerosol using the molar fraction approach was similar to the result obtained by (Stelson, 1990) who used this method to calculate an aerosol particle density of 1.4 g/cm3 up to 1. 7 g/cm3. Experimental measurements, conducted by Stein et al. (1994), also showed a similar average density of 1.6 to 1. 79 g/cm3, for a range of relative humidity between 10 - 60 %. Unfortunately, there was no independent method to validate the accuracy of the two approaches. However, since both result agree to within - 15 % of one another, and with other reported work, we believe that both methods will be applicable within a reasonable accuracy. 4. 7 Summary and conclusion In the present work, a thermodynamic model, SCAPE2, was used to predict the inorganic chemical composition and the water contents of atmospheric aerosol, using the elemental composition of particles, measured by IBA analysis, for Sydney aerosol. Results were used to predict the growth of aerosol particle as a function of relative humidity, a critical factor for ambient aerosol measurement. An empirical model of the growth of aerosol at different relative humidities was calculated for each month, and for the whole year. Maximum growth of aerosol particle was noticed during the winter season, while minimum growth occurred during summer season. The use of the thermodynamic model SCAPE2 proved to be a good alternative to the experiment to predict a reasonable approximation of the aerosol particle growth, based on its chemical composition. Calculation of refractive index and particle density was performed as a function of relative humidity, using molar fraction and mass fraction approaches. Both methods agreed reasonably well with each other. Results of such calculation yielded a refractive index of 1.73 to 1.51 for a humidity range of 0.3 to 0.9. The aerosol particle density varied between 1.9 g/cm3, and 1.44 g/cm3, for the same range of relative humidity. The resultant density using mass fraction approach was higher by - 0.2 g/cm3. Results of this work will provide valuable information about the inorganic chemical composition, refractive index and density of ambient aerosol, as well as the impact of relative humidity on aerosol properties. Such information will improve the Chapter 4 The Use of Thermodynamic Model ... 119 accuracy of the modelled aerosol optical and physical properties. It will also improve the accuracy of tests of closure or consistency studies between remote sensing and in situ measurements as well as the models that link them. For the future, an experiment that involves ambient nephelometer measurements at different relative humidities, will provide an experimentally determined growth factor, which can be compared with the value calculated here using SCAPE2. This will enable us to test the accuracy of the assumptions made here, and improve the accuracy of the calculation. The availability of the size resolved chemical composition of the aerosol particle, such as MOUDI measurements, would enable us to calculate a size resolved growth factor, needed for size distribution closure studies between remote sensing and is situ measurements. As it is known, different size ranges have different constituents, and hence different growth factors. Chapter 5 Size Distribution Of Aerosol Particles 5.1 Introduction Atmospheric aerosol particles m the troposphere, and to a lesser extent, in the stratosphere, can influence the climate directly, by scattering and absorbing the incoming solar radiation, and indirectly by modifying the optical properties and lifetime of clouds. This radiative effect is strongly dependent on the particle size distribution and composition, rather than simply on the total mass loading alone. To study aerosol size characteristics and composition, measurements have to be performed frequently as its properties vary with the change of its source, meteorological conditions and age. Spectral radiation observations remain one of the widely used measurements to study aerosol optical and physical properties. In particular, spectral resolution of the aerosol extinction measurements enables the estimation of aerosol size distribution in the vertical column. The spectral variation of the aerosol optical thickness is mainly determined by the aerosol size distribution. Assuming homogeneous spherical aerosol particles, which are nondispersive over the wavelength range of observations, and known refractive index, the aerosol optical thickness is related to the size distribution by the following integral equation, (5.1) where i-.,,(A) is the optical thickness at wavelength A, Qe is the Mie extinction efficiency factor, m is the complex refractive index, r is the particle radius and n(r) is the number of particles per unit area per unit radius in a vertical column through the atmosphere, with radii in the size range between r and r + dr. The above equation is a Fredholm integral and must be inverted numerically to obtain the aerosol size distribution, n(r). The inversion of this type of integral equation is an ill-posed problem, that is there is no mathematically unique solution. Over the years, there have been a number of different inversion techniques employed to solve this problem, Chapter 5 Size Distribution ofAerosol Particles 121 including constrained linear (Twomey, 1963; Yamamoto and Tanaka, 1969; King et al., 1978; King, 1982), iterative (Heintzenberg et al., 1981; Trakhovsky et al., 1982) and analytic eigenfunction theory (Viera and Box, 1985; Box et al., 1992; Box, 1995). In this chapter, we investigate the use of the constrained linear inversion technique and analytic eigenfunction theory to obtain the aerosol size distribution, using both synthetic data and real measurements obtained using the MultiFilter Rotating Shadowband Radiometer (MFRSR). The results obtained are used to study the seasonal behaviour of aerosol particles in Sydney, and the effect of relative humidity on particle size distribution. 5.2 Constrained linear inversion This inversion technique is referred to as constrained linear inversion because it depends on introducing an additional condition or criterion, not driven from measurements, which discriminates against instability, and enables one of the set of non-oscillatory and satisfactory solutions to be selected from all the mathematically possible solutions. An obvious constraint to the problem of the aerosol size distribution retrieval would require the solution to be positive, and the smoothest among other possible solutions. One of the most used constraints is in the form of a smoothing constraint, in which the sum of the squares of the second derivative of the solution points is minimised. Another popular constraint is minimising the sum of the squares of the solution points, so that the constraint matrix becomes the identity matrix. The formulation of this problem is one in which a non-negative Lagrange multiplier is introduced as a means of varying the relative contribution of the kernel matrix without changing the constraint matrix. 5.2.1 Solution description We may write the generic form of the Fredholm integral of the first kind as b g(y) = JK(x,y)f(x)dx + e(y) (5.2) a where K(x,y) is the kernel function, g(y) is the set of measurements, e(y) are the measurement errors, and f(x) is the unknown function we wish to retrieve. The expressionf(x) cannot generally be written analytically as a function of g(y), and thus the equation must be solved numerically. If there are measurements of g(y) at p Chapter 5 Size Distribution ofAerosol Particles 122 discrete values of y, and we want to infer f(x) at p discrete values of x, then the above expression can be expressed as a system of linear equations g=Af+e (5.3) where A is a p x q square matrix of weights b Aij = fK;(x)dx,(i = 1,2, ..... ,N) a Twomey (1977) studied measures of smoothness and showed that generally, the measure of smoothness can be written as fTHf, where H is usually a simple near diagonal matrix and f T is transpose matrix of f. The solution vector f is then obtained by minimising the quadratic form Q, which is the sum of two terms, the first is the measure of error associated with the measurements, while the second is a measure of smoothness. King (1982) defined Q as (5.4) where p p Q, = eTS~'e = LLe;s;.'e1 i=l J=l q q Q2 = fTHf = LLJ;Hij~ i=I J=I where y is a non-negative Lagrange multiplier, Se is the measurement covariance matrix and se-l is the inverse matrix of Se. The solution vector f, where Q is minimised, is given by (5.5) If the statistical errors in the measurements were assumed to be equal and uncorrelated, then Se is reduced to s2I, where s2 is the sample variance for the regression fit, and I is the identity matrix. To find the solution of the above problem, Equation (5.5), it is necessary to Chapter 5 Size Distribution ofAerosol Particles 123 select a proper value of y which produces the desired smoothing. King et al. ( 1978) based their selection of yon the magnitude of y HkJ I (ATs/ A)kJ, rather than just y, since it enters (5.5) in a fashion such that the elements of ,II are to be added to ATse- 1A to produce the required smoothing. Therefore, it would be convenient to vary Yrel = H11 / (ATse- 1A)11 in the range of 10-3 to 5, until a minimum value of Yre1 is reached for which all elements off are positive. The next step is to obtain a solution covariance matrix, S. King (1982) obtained a solution S from the inverse of the curvature matrix, a-1, which is related to the curvature of Q hyper surface in coefficient space. Elements of a are given by 1 2Q a---a kl - 2 i!fk rJf, The solution covariance matrix is given by (5.6) In the absence of measurement uncertainties, an approximation of s2, the sample variance for the regression fit is given by 1 P s2 =--~:>·; (5.7) p-q i=I Which means that the solution covariance matrix becomes (5.8) 5.2.2 Application of the technique to the inversion of aerosol size distribution problem For the retrieval of aerosol size distribution from spectral extinction measurements, using constrained linear inversion remains one of the favourite, and widely used in many studies, such as (Twomey, 1963; Yamamaoto and Tanaka, 1969; King et al., 1978; Russell, et al., 1993; Valiente, 1996; Pandithurai, et al., 1997). To determine the aerosol size distribution, King et al. (1978) separated n(r) into Chapter 5 Size Distribution ofAerosol Particles 124 two parts, n(r) = h(r) f(r), where h(r) is a known rapidly varying function of r, and f(r) is an unknown slowly varying function. Therefore, equation (5.1) is expressed as q Taer(A) = L f·' trr 2Qe(r,A,m)h(r)f(r)dr (5.9) J=I 1 f(r) is assumed to be constant within each coarse q interval. The result will be a system of p linear equations and q unknowns of fj, in the form of Equation (5.3), which can be expressed as Elements of A are given by j+I 2 1 h d A!i = [ 1!r Qe(r,A;,m) (r) r, 1 i = 1,2,, ...... ,p,j = 1,2,, ...... , q and r1~r ~rJ+J. In the case of the MFRSR, q = 5, and the second derivative smoothing matrix H is given as follows 1 -2 1 0 0 -2 5 -4 1 0 H= 1 -4 6 -4 1 (5.10) 0 1 -4 5 -2 0 0 1 -2 1 The first step of the solution is to calculate the Junge exponent from the wavelength dependent aerosol optical thickness using the relationship proposed by Angstrom (1929) (5.11) where a is the wavelength exponent or Angstrom coefficient, which is related to the size distribution. The Junge size distribution, which will be assumed as zero-order weighting function in the form of (Junge, 1955) Chapter 5 Size Distribution ofAerosol Particles 125 (5.12) Junge exponent is given by v * = a+ 2. h(O) (r) is used as an initial guess, and the first 2 order J(ll(ij) is computed using Equation (5.5),ij = hrk )1' • The first order/1l (r) is a modifying factor to the initial guess, and hence the first-order weighting function is calculated as The above procedure is repeated in iteration until a stable solution is obtained. Performed iteration procedures are not to exceed eight times (King, 1982). Uncertainties of the complex refractive index have been shown by (King et al., 1978), not to be so critical, with slight effect on the inverted aerosol size distribution, and thus a refractive index of m = 1.5 - 0.0li is assumed. The choice of the radii limit can be critical in some cases, where the size range is far from the optimum one, then it may not be possible to obtain a solution where all elements off are positive (King, 1982). In general, inversion will be performed at 16 radii, (q = 16), within the range 0.1 $ r $ 4.0 µm. In other cases, mainly when a is low or negative, no positive solution is possible for this range, and therefore a different size range will have to be used on a trial and error basis. 5.3 Analytic Eigenfunction theory Different approach was followed by McWhirter and Pike (1978), who developed a theory to invert the Fredholm integral equation, expressed in a generic form as 00 g(x) = JK(x,y)f(y)dy (5.13) 0 Analytic eigenfunction theory will apply only to product kernels in the form K(x,y) = K(xy). This is equivalent to the original form of size distribution (5.1) as the Mie theory extinction kernel, Qe(r,A,m), can be expressed as a product kernel in the form Qe(kr,m), for a constant m over the wavelength range. Qe is a function of the size parameter k = 2ti I A only. Chapter 5 Size Distribution ofAerosol Particles 126 The eigenfunctions and eigenvalues of this product kernel are defined as 00 JK(xy)q>a)x)dx = A(w)q>w(Y) (5.14) 0 A( w) and q) w (y) are calculated usmg Mellin transform of the above equation as (McWhirter and Pike, 1978) (5.15) q>w(Y) = y(-1/2)-iw x(w), (5.16) where x(w) = [K(l/2- + iw) I1rA(w) ]t/2 and 00 K(l/2 + iw) = Jt-(I/Z)+iw K(t)dt (5.17) 0 is the Mellin transform of the kernel K. This method was applied for size distribution retrieval from optical thickness values by (Viera and Box, 1985) and later (Box et al., 1992). The retrieval of f(y) requires effectively expanding bothf(y) and g(x) in terms of the eigenfunctions q) as Wm f(y) = y-112 Re fG(w)y-iw z(w)/A(w)dw (5.18) 0 where 00 G(w) = z(w) jg(x)x(-vi)-iw dx. 0 To satisfy convergence requirements, the kernel is selected as (5.19) where t= xy = kr. Identifying y with r and x with k, Chapter 5 Size Distribution ofAerosol Particles 127 f(y) => r 3n(r), (5.20) Therefore, Equation (5.13) is expressed as follows - k-1 r(k) = J[ nQe( kr)/kr][r3n(r) ]dr (5.21) 0 The next step requires obtaining Mellin transform of K(t) and K(l/2 + iw), using numerical integration of Equation (5.17), for values of w from 0~ 10, and a refractive index of m = 1.5 - 0.0li. To improve the accuracy of inversions of Mie extinction measurements with analytic eigenfunction theory, Box (1995) included higher order eigenvalues and their corresponding eigenfunctions using a smoothing parameter S, with values between 0.0 and 0.10. The smoothing of the eigenvalue spectrum will reduce the error magnification, caused by the inclusion of smaller eigenvalues. Therefore, Equation (5.18) becomes - f(y) = y-112 Ref G(w)y-iw z(w)/A'(w)dw (5.22) 0 where I A(w) =----- ).'(w) A2 (w) + S).(0)' A(O) is the largest eigenvalue, and ~ is the highest order eigenfunction included. The inclusion of a smoothing parameter allows the value of ~ to be increased and reduces the risk of severe oscillations as a result of errors in g(x). Box et al. (1992) were able to retrieve aerosol size distribution from aerosol optical depth that were synthesised from different types of aerosol size distributions, and for El Chicon volcanic extinction measurements, made at 10 wavelengths between 0.4 µm and 2.233 µm, using analytic eigenfunction theory. However, measurements of the MFRSR are only available for 5 wavelengths in the range of 0.40 - 0.86 µm, and that could affect the retrieved size distribution (Box, 1995). Chapter 5 Size Distribution ofAerosol Particles 128 5.4 Testing procedure Before applying either of the methods described above, to the actual attenuation measurements, some numerical experiments were carried out to test the efficiency of both retrieval techniques. To do so, several types of simulated optical thickness data, assuming lognormal distributions, were generated, and used for the inversion process. The lognormal distribution is given by 2 n(r) = -expA [ --1 {ln(r/rg)}-- ] , (5.23) r 2 C where rg is the median radius, c controls the distribution width, A is a normalisation factor given by and rg is related to the volume-weighted mode radius, rm by If dN/dlogr distribution is used, then rg = rm, For this study, 4 models of aerosol were considered and summarised in Table 5.1. The first two represent a unimodal aerosol of smaller particles. The third and fourth are bimodal aerosol, with a large secondary mode, more representative of the tropospheric aerosols. Aerosol size distribution of the 4 different models of aerosol was retrieved using both the constrained linear and the analytic eigenfunction inversion techniques. The calculations were carried out using only the information at wavelengths similar to the MFRSR measuring channels (J =0.415, 0.502, 0.616, 0.673, 0.870 µm). Table 5.1: Summary of the 4 different types of simulated aerosol lognormal size distribution, where rm is the volume weighted mode radius, rg is the median radius, and c is the parameter that controls the distribution width. Aerosol Type Mode radius, r m Median radius, rg C Stratospheric 0.16 0.08 0.60 Uni.modal 0.25 0.15 0.50 Bimodal I 0.25 & 1.0 0.15 & 0.61 0.50 & 0.50 Bimodal 2 0.25 & 2.0 0.15 & 1.21 0.50 & 0.50 Chapter 5 Size Distribution ofAerosol Particles 130 5.4.1 Constrained linear In the case of constrained linear inversion, Figures (5.1, 2, 3 and 4) show both the real and retrieved aerosol size distribution dN/dlogr in (cm-2) versus r in (µm), in a log-log scale. The distributions are in the same order as in Table 5.1. The retrieved aerosol size distribution was generally accurate compared to the true distribution. In all four cases, the fine mode position and width was retrieved accurately. At the same time, the retrievals at r::; 0.1 µm were not very accurate, as expected, from the work of (King et al., 1978). This was particularly apparent in both bimodal cases, where retrieval at radius r = 0.1 µm clearly underestimated the actual distribution. In the case of the larger modes, the position and the width of the mode rg= 0.61 µm (Figure 5.3) was located accurately, despite the slight overestimation of the last two points of the inversion. On the other hand, the position of the mode rg = 1.2 µm (Figure 5.4) was not detected as clearly as in the previous case, although one can still recognise the position of the mode. The magnitude and spread of the mode was not so accurate, as for larger radii, r> 2.0 µm, the distribution began to increase, instead of decreasing, and becomes less accurate. Results of inversion of the simulated aerosol distribution, presented above, will place increased confidence on the use of the constrained linear technique for the problem of inverting the aerosol optical thickness data collected in Sydney by the MFRSR, to obtain the aerosol size distribution, without the need of any extra measurements. All the four cases of synthetic aerosol types discussed here, which were retrieved with reasonable accuracy, are representative of Sydney aerosol, with the last model of rg = 1.20 µm least likely to occur, as we will show in the next section. Chapter 5 Size Distribution ofAerosol Particles 131 1.E+12 1.E+10 .... 1.E+8 0) 0 "'O 1.E+6 -z "'O 1.E+4 inverted 1.E+2 1.E+0 0.01 0.1 1 10 r, radius (µm) Figure 5.1: Simulated and retrieved aerosol size distribution dN/dlogr ( cm·2), using constrained linear inversion technique, versus radius, r (µm), in log-log scale for stratospheric aerosol of median radius rg = 0.08µm, and c = 0.6. 1.E+10 /""'?p*,,.,•), 1.E+8 .... 1.E+6 0) 0 "'O 1.E+4 -z "'O 1.E+2 inverted 1.E+0 1.E-2 0.01 0.1 1 10 r, radius (µm) Figure 5.2: Simulated and retrieved aerosol size distribution dN/dlogr (cm·2), using constrained linear inversion technique, versus radius, r (µm), in log-log scale for monomodal aerosol of median radius rg = 0.I5µm, and c = 0.5. Chapter 5 Size Distribution ofAerosol Particles 132 1.E+10 1.E+9 '- 0) 0 "'C 1.E+8 -z "'C 1.E+7 1.E+6 ---...... ~---~'"""I----..._...... , 0.01 0.1 1 10 r, radius (µm) Figure 5.3: Simulated and retrieved aerosol size distribution dN/dlogr (cm·2), using constrained linear inversion technique, versus radius, r (µm), in log-log scale, for bimodal aerosol. First median radius rg = 0.15 µm, and c = 0.5, second median radius rg = 0.61 µm, and c = 0.5. 1.E+9 '- 0) 0 ~ 1.E+8 z "'C 1.E+7 -*-inverted 1 . E +6 ------...... ~-----__._ ...... _ __._ ...... 0.01 0.1 1 10 r, radius (µm) Figure 5.4: Simulated and retrieved aerosol size distribution dN/dlogr (cm·2), using constrained linear inversion technique, versus radius, r (µm), in log-log scale, for bimodal aerosol. First median radius r m = 0.15 µm and c = 0.5, second median radius rg = 1.2 µm, and c = 0.5. Chapter 5 Size Distribution ofAerosol Particles 133 5.4.2 Analytic eigenfunction theory The second part of this test involved the use of analytic eigenfunction theory to invert the same simulated aerosol optical thickness data as in the previous part. The results presented here are for mii = 7, and a smoothing parameter S = 0.01, since it produced the best outcome. Figures (5.5, 6, 7, and 8) are as for Figures (5.1, 2, 3, and 4) but with the analytic eigenfunction theory used as the inversion method. The fine mode was retrieved with reasonable accuracy for all the types of aerosol, however, in the first case of stratospheric aerosol, the position of the mode was shifted from rg =0.08 into 0.25 µm. In general, for all cases, the retrieval for r < 0.10 µm was very poor. For the case of a bimodal aerosol, the bimodal nature of the distribution is distinguishable, nevertheless, the location and the magnitude of the larger mode was very poor. The mode was shifted to a smaller radius, and its value was overestimated. It is obvious that for this range of measurement, (0.4 ~ ').. ~ 0.86 µm), the analytic eigenfunction theory, is capable only of retrieving size distribution of smaller particles, as the information available to retrieve the distribution larger size is not enough. In a previous work, Box (1995) studied the effect of the measurement wavelength range on the accuracy of the analytic eigenfunction inversions, and reached a similar conclusion. She found that for a successful retrieval of larger particle, measurements in the infrared were necessary. 5.4.3 Effect of adding extra information To study the effect of adding extra information on the quality of the retrieved distribution, we calculated the optical thickness of the last case of bimodal aerosol with the largest mode rg = 1.21 µm, at three extra wavelengths,')..= 0.381, 1.571 and 2.244 µm. The result improved significantly for the inverted distribution using the Chapter 5 Size Distribution ofAerosol Particles 134 1.E+11 1.E+10 .... 1.E+9 C> 0 1.E+8 "C ---z 1.E+7 "C 1.E+6 -real 1.E+5 1.E+4 0.01 0.1 1 10 r, radius (µm) Figure 5.5: Simulated and retrieved aerosol size distribution dN/dlogr (cm"2), using analytic eigenfunction inversion technique, versus radius, r (µm), in log-log scale, for stratospheric aerosol of median radius rg = 0.08 µm, and c = 0.6. 1.E+10 1.E+9 .... 1.E+8 C> 0 "C 1.E+7 ---z "C 1.E+6 1.E+5 1.E+4 0.01 0.1 1 10 r, radius (µm) Figure 5.6: Simulated and retrieved aerosol size distribution dN/dlogr (cm·2), using. analytic eigenfunction inversion technique, versus radius, r (µm), in log-log scale, for monomodal aerosol of median radius rg= 0.15 µm, and c = 0.5. Chapter 5 Size Distribution ofAerosol Particles 135 1.E+11 1.E+10 L.. C> 0 1.E+9 "'O -z 1.E+8 "'O 1.E+7 --real 1.E+6 0.01 0.1 1 10 r, radius (µm) Figure 5.7: Simulated and retrieved aerosol size distribution dN/dlogr (cm-2), using analytic eigenfunction inversion technique, versus radius, r (µm ), in log-log scale, for bimodal aerosol. First median radius rg = 0.15 µm and c = 0.5, second median radius rg = 0.61 µm, and c = 0.5. 1.E+11 1.E+10 L.. g> 1.E+9 ~ ~ 1.E+8 -real 1.E+7 -*-inverted 1. E+6 __.....__...... ""'""+___...... _ ...... , 0.01 0.1 1 10 r, radius (µm) Figure 5.8: Simulated and retrieved aerosol size distribution dN/dlogr (cm-2), using analytic eigenfunction inversion technique, versus radius, r (µm), in log-log scale, for bimodal aerosol. First median radius rg =0.15 µm and c = 0.5, second median radius rg = 1.20 µm, and c = 0.5. Chapter 5 Size Distribution ofAerosol Particles 136 analytic eigenfunction inversions, Figure 5.9, while it remained almost unchanged for the constrained linear inversion, Figure 5.10. It is obvious that the extra information did not improve the aerosol distribution retrieved using the constrained linear. That is because the technique uses the Junge distribution as a first guess. The Junge distribution is calculated using Angstrom coefficient, a, which describes the aerosol optical thicknesses as a function of the wavelength. In other words, informations of aerosol optical thickness, other than the measurements, are already included. The prediction of optical thicknesses from the measurements can be an excellent and cheap alternative to the addition of extra channels to the existing measurements of the MFRSR, or any other instrument with such limited wavelength range. This extra information is required to improve the size distribution retrieval using the eigenvalues theory. Box and Box (1999) in a preliminary study investigated the use of kernel covariance matrix to predict the aerosol optical thicknesses beyond the measurement range using available measurements, and found that it will improve the quality of the retrieved distribution usmg the eigenvalues theory significantly. However, they concluded that more work is required to refme this technique, and defme its limitations. Therefore, at this stage, and based on the above comparison between the two techniques, constrained linear inversion technique will be the best option to invert the optical thickness measured by the MFRSR, to describe the Sydney aerosol size distribution. 5.5 Results and discussion Measurement of total optical thickness has been carried out at University of New South Wales, Sydney since early 1996, using the Multifilter Rotating Shadowband Radiometer (MFRSR). Aerosol optical thickness was determined, after correcting for gaseous absorption and molecular scattering at wavelength of A= 0.415, 0.502, 0.616, 0.673, 0.870 µm (see chapter 2 for details). Aerosol size distribution was calculated using the constrained linear inversion technique. A total of 23 7 sets of data, Chapter 5 Size Distribution ofAerosol Particles 137 1.E+11 1.E+10 ... g> 1.E+9 :E ~ 1.E+8 -real 1.E+? 1. E+6 +-...... ,___,__._._ ...... +--...... ,_ ...... "'""' 0.01 0.1 1 10 r, radius (µm) Figure 5.9: As in Figure 5.8, but for the retrieved size distribution with 3 more wavelengths added. The extra information is at A= 0.381, 1.571 and 2.244 µm. ... 1.E+9 C) 0 1.E+8 :Ez "O 1.E+7 -real --e- inverted 1.E+6 __...... __...... ____ ...... 0.01 0.1 1 10 r, radius (µm) Figure 5.10: As in Figure 5.4, but for the retrieved size distribution with 3 more wavelengths added. The extra information is at A= 0.381, 1.571 and 2.244 µm. Chapter 5 Size Distribution ofAerosol Particles 138 of either morning or afternoon, were successfully inverted. The results presented here cover the period from October 1, 1996 until September 16, 1997. 5.5.1 Aerosol size distribution results In most of the cases we have studied, aerosol size distribution can be classified into three types of distribution, bimodal, unimodal, and power-law or Junge distribution. The bimodal distribution was the dominant aerosol distribution type that was observed. It comprised 82 % of the successfully inverted data, while the unimodal distribution was observed occasionally, in nearly 10.5 % of the data, and power-law distribution type was only observed in 7 .5% of the cases. Mode radii of the bimodal aerosol were rm = (0.10 - 0.35) µm and rm = (0.6 - 0.85) µm, while it was rm = (0.10 - 0.35) for the unimodal distribution. The majority of the unimodal and power-law aerosol distributions were observed in the morning, during the winter season, (July, August and September). In order to fully understand the nature of the aerosol size distribution, we need to study the spectral dependence of aerosol optical thicknesses too, as its behaviour over the wavelength range will almost exclusively define the nature of the resultant size distribution. Therefore, we are going to present different cases of aerosol size distribution, according to the behaviour of the observed aerosol optical thickness over the wavelength range and its effect on the inverted distribution. The first case represents a typical aerosol type of bimodal distribution, which was observed for the majority of the examined sets of measurements. Examples of this type are illustrated in Figure 5.11 (a), which is a plot of the measured aerosol optical thickness, Taer, versus the wavelength, A(µrn), for the morning of November 3, December 16 1996, and February 28 1997, while Figure 5.12 (a) is for the afternoon of October 13, November 14 1996, and February 28 1997. The curve is the best fit of the calculated values of the optical thicknesses using the inverted size distribution, the only available measure of the accuracy of the inversion. (b) is a plot of the inverted aerosol columnar size distribution, dN/dlogr( cm-2), or the number of particles per unit area per unit log radius interval in the vertical column through the atmosphere, versus the particle radius, r (um). The inversion was executed for a size range of 0.1~ r ~ 4.0 µm. The spectrally resolved aerosol optical thickens measurements followed a typical 1 1.E+9 (a) 1.E+8 (b) '"ii ~ 1.E+7 ui Ill 1.E+6 Q) C: ts ~ 1.E+5 :2 1.E+4 I- 0.1 f al o, 1.E+3 0 )K 0 +3/11/96AM "' ...... ;)K a ~,:,: ~ 1.E+2 0 z 0 0 16/12/96 AM ',,,_ "O 1.E+1 Ill --+- 3/11/96 AM, V*=2.95 J:28/2/97 AM eQ) '0-,0--. 1.E+0 <( ------o -0-16/12/96 AM, V*=3.65 1.E-1 ~ 28/2/97 AM, V*=2.33 0.01 1.E-2 0 0.2 0.4 0.6 0.8 1 1.E-3 0.1 1 10 Wavelength,').. (µm) r, radius (µm) Figure 5.11 :(a) Measured aerosol optical thickness, 'taer, plotted as a function of wavelength, A(µm), for the morning of November 3, 1996, December 16, 1996, and February 28, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) Is the aerosol size distribution calculated for the same days as (a) plotted as function of radius, r (µm). 1 1.E+9 (a) ~ ~ 1.E+B (b) ~ "' ui 1.E+7 Cl) Q) C: - 1.E+6 ""(J :c "'"E I- 0.1 ~ 1.E+5 cii ... (J Cl 0 1.E+4 a X 32 0 ·0 z 0 X 14/11/96 PM "O 1.E+3 eCl) Q) 028/2/97 PM --+-13/10/96 PM, v*=3.27 <( 1.E+2 14/11/96 PM, v*=2.51 1.E+1 ~ 0.01 -8- 28/2/97 PM, v*=2.99 0 0.2 0.4 0.6 0.8 1.E+0 0.1 1 10 Wavelength, "A(µm) r, radius (µm) Figure 5.12:(a) Measured aerosol optical thickness, 'taer, plotted as a function of wavelength, "A(µm), for the afternoon of October 13, 1996, November 14, 1996, and February 28, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) Is the aerosol size distribution calculated for the same days as (a) plotted as function ofradius, r (µm). Chapter 5 Size Distribution ofAerosol Particles 141 exponential distribution, with positive Angstrom coefficient and a slight positive curvature, where the optical thickness decreases with wavelength. The second case, Figure 5.13, for the morning of January 20, February 3, and July 10 1997, and Figure 5.14 for the afternoon of May 1, August 3, and 24 1997, represents a situation that occurred occasionally, and produced similar bimodal distribution, with a positive Angstrom coefficient, where the measured optical thickness had a tendency to decrease with wavelength forming a negative curvature with a peak. This case was difficult to invert, and only after several trials and errors, data was successfully inverted using a size range of (0.20 - 0.50µm)~ r ~ (1.90 - 4.0µm). Case 3, illustrated in Figure 5.15 for the morning of June 24, August 22, and September 3 1997, and Figure 5.16 for the afternoon of April 26, May 28, and September 15 1997, produced a power-law size distribution which is characterised by a monotonic decrease in n(r) with increase in aerosol radius, r. This type of aerosol produced a positive Angstrom coefficient. The observed optical thickness decreased with the wavelength, similar to the first case, but it produced a near linear slope. Case 4, Figure 5.17 for the morning of June 21, July 17, and August 10 1997, and Figure 5.18 for the afternoon of April 12, July 6, and August 8 1997, showed a unimodal aerosol type with narrow distribution, as particles numbers dropped significantly at r ~ 1.0 µm. The optical thickness measurements over wavelength yielded a negative curvature, similar to case 2, but with a fairly broad peak. To invert Cases 3 and 4, similar size range to the one used for case 1 and 2, respectively, had to be used. In case 5, measurements of negative Angstrom coefficient are considered, where optical thickness increases with wavelength. The aerosol optical thicknesses that usually produce negative coefficient are usually small. In Figure 5.19 for the morning of December 22 1996, July 18, and August 14 1997, the optical thickness measurement produced a negative curvature, and the resultant inversion was unimodal distribution. This behaviour was observed occasionally during the morning, and none was measured during the afternoon. Not all optical thickness measurements which display a negative Angstrom coefficient show a unimodal distribution, as measurements illustrated in Figure 5.20 for the morning of November 18 1996, January 1, and August 18 1997, and Figure 5.21 for the afternoon of January 20, February 3, and April 6 1997, produced a similar negative Angstrom coefficient, but 1 (a) 1.E+8 (b) 1.E+7 ~., ~ ui 1.E+6 C/l Q) C ~ ~1.E+5 u 'E :c ~1.E+4 I- 0.1 .... 0.01 1.E-1 0 0.2 0.4 0.6 0.8 1 0.1 10 Wavelength,'). (µm) r, radius (µm) Figure 5.13:(a) Measured aerosol optical thickness, 'taer, plotted as a function of wavelength, A(µm), for the morning of January 20, 1997, February 3, 1997, and July 10, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) is the aerosol size distribution calculated for the same days as (a) plotted as function of radius, r (µm). 1 I 1.E+8 (a) (b) l- 1.E+7 ~ --·"-" --:;; I ~ vi L I 1.E+6 rn Cl) C ~ ~1.E+5 (.) 'E :2 ~1.E+4 I- 0.1 ... iii Cl (.) :@1.E+3 a --- 0 X 1/5/97 PM ~ 1.E+2 1 ~ 1/5/97 PM v*=2.38 0 • • rn •• • e +3/8/97 PM ijc--1!1·----~- _ 1.E+1 ! -+-318/97 PM, v*=2.12 Cl) O 24/8/97 PM (S. ____ <( --x ----o 1.E+0 -t -0-24/8/97 PM v*=2.57 0.01 1.E-1 0 0.2 0.4 0.6 0.8 1 0.1 1 10 Wavelength,). (µm) r, radius (µm) Figure 5.14:(a) Measured aerosol optical thickness, "taer, plotted as a function of wavelength, A(µm), for the afternoon of May l, 1997, August 3, 1997, and August 24, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) is the aerosol size distribution calculated for the same days as (a) plotted as function ofradius, r (µm). 1 ~ 1.E+9 1.E+8 -:;; (a) -0-24/6/97 AM, V*=3.59 ~ ···~ ui 1.E+7 f C/l __._22/8/97 AM, V*=3.66 Q) 1.E+6 C 0.1 .ll:: N (.) - :2 j 1.E+S I- 024/6/97 AM Wavelength,).. (µm) r, radius (µm) Figure 5.15:(a) Measured aerosol optical thickness, 'tacr, plotted as a function of wavelength, A(µm), for the morning of June 24, 1997, August 22, 1997, and September 3, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) is the aerosol size distribution calculated for the same days as (a) plotted as function of radius, r (µm). 1 I 1.E+9 1.E+8~ -+- 26/4197 PM, v*=3.96 -t;; (a) ~ 1.E+7 { -*- 28/5197 PM, v*=4.3 ui K ~ rn Q) X 1.E+6 C: .lo:: 'l' (.) - :c 'Kx f 1.E+5 I- 0.1 + 26/4/97 PM O. 5>1.E+4 m(.) 0 ~ X28/5/97 PM ··o..... a. ~1.E+3 '·O.. o. 0 O 15/9/97 PM ··-.. 0 -o 1.E+2 ern ···o Q) 1.E+1 t <( (b) 1.E+0 0.01 1.E-1 0 0.2 0.4 0.6 0.8 1 0.1 1 10 Wavelength,').. (µm) r, radius (µm) Figure 5.16:(a) Measured aerosol optical thickness, 'taer, plotted as a function of wavelength, A(µm), for the afternoon of April 26, 1997, May 28, 1997, and September 15, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) is the aerosol size distribution calculated for the same days as (a) plotted as function ofradius, r (µm). 1.E+8 -..------, (a) 1.E+7 1- -+-2116/97 AM, V*=3.03 ui 1.E+6 Cl) (I) C: 0.1 ~ c-1.E+5 (,) 'E :c S.1.E+4 I- +21/6/97 AM ... Cl ~ ;iic17/7/97 AM :;::i :§ 1.E+3 a. 0 010/8/97 AM ~....~ % z 0.01 !i_._ - -c 1.E+2 0 X···--- o Cl) e 0 1.E+1 (b) ~ 1.E+0 0.001 _.,__...... -+-'-...... -+-'- ...... -+-'- ...... +-'- ...... , 1.E-1 +- _ __.._...... _...... ,...... _...... '-11-.li-----'-...... _...... _...... _...... ,.....,. 0 0.2 0.4 0.6 0.8 1 0.1 1 10 Wavelength, 'A (µm) r, radius (µm) Figure 5.17:(a) Measured aerosol optical thickness, 'taer, plotted as a function of wavelength, A(µm), for the morning of June 21, 1997, July 17, 1997, and August 10, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) is the aerosol size distribution calculated for the same days as (a) plotted as function ofradius, r (µm). 10 1997, 6, v*=3.34 v*=5.6 v*=3.36 July PM, PM, PM, 1997, 12, 12/4/97 distribution. (µm) (µm). size April 1 r -0-6/7/97 ~ ~B/8I97 of radius r, radius, inverted of the afternoon the using function for as ___.__..__.....__...... _....,_~------...... _...... _...... ,....._....._. A(µm), thickness _ -----.------. plotted (b) +- ...... 0.1 (a) optical as 1.E-1 1.E+0 1.E+3 1.E+2 1.E+8 1.E+1 1.E+7 1.E+6 wavelength, days Cl ... 'E z -c :§ ~1.E+4 of N'1,E+5 calculated same the the 1 function of for a ...... , fit . 0 ...... plotted as 0.8 calculated ~ . regression ... 'tacr, . the 0- . is (µm) ...... Q_ 0.6 'A .. line distribution ~ thickness, size curve cf_Q ~ optical 0.4 The aerosol Wavelength, PM PM PM the ...... 1997. aerosol is 8, +-'- 0.2 (b) 12/4/97 06/7/97 + ::(8/8/97 August ...... Measured (a) 0 -tc +-- 't and 1------, 0.1 5.18:(a) 0.01 0.001 0.0001 ~ Figure rJi Ill Q) (.) e ~ ~ I- ~ ~ 0 :E :g_ 0 - 1 ~ I 1.E+7 (a) X22/12/96 AM 1.E+6 -X--- 22/12/96 AM, V*=l.44 -..lo I ...t,I. +18/7/97 AM ui 1/) O 14/8/97 AM 1.E+5 Q) C 0.1 .II:: 14/8/97 AM, V*=l.98 (.) ~ 1.E+4 :c I- ~ cii 5i1.E+3 (.) 0 ~ •---·::> .2-- "S<- ~----"""""·'· I I I I I I I I I I I I I I I I I 0.001 t I .I 1.E-1 0 0.2 0.4 0.6 0.8 1 0.1 1 10 Wavelength,).. (µm) r, radius (µm) Figure 5.19:(a) Measured aerosol optical thickness, 'taer, plotted as a function of wavelength, A(µm), for the morning of December 22, 1996, July 18, 1997, and August 14, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) is the aerosol size distribution calculated for the same days as (a) plotted as function of radius, r (µm). 1--.------, 1.E+7 --.,------r::r------, (a) (b) -J 1.E+6 ui rn 1.E+5 Q) C: .:it:. (.) ~1.E+4 :c I- ~ 0.1 c,1.E+3 ~ 0 :g_ ~ z 1.E+2 0 "C -0-18/11/96 AM, 0 0 18/11/96 AM /X * )K X V*=l.47 rn x o -oo-~o 1.E+1 e +511/97 AM 6 .-_....··· --+-5/1/97 AM, V*=l.58 ~ )1(18/8/97 AM - 1.E+0 _._ 18/8/97 AM, V*=l.74 1.E-1 ___ ...... _..__ ...... __.______...... _ 0.01 • __ 0 0.2 0.4 0.6 0.8 1 0.1 10 Wavelength, 'A(µm) r, radius (µm) Figure 5.20:(a) Measured aerosol optical thickness, 'taer, plotted as a function of wavelength, 'A(µm), for the morning of November 18, 1996, January 5, 1997, and August 18, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) is the aerosol size distribution calculated for the same days as (a) plotted as function of radius, r (µm). 1 1.E+7 (a) ~ 1.E+6 '(b) a; ~ I ~ ui U) 1.E+S (I) C: .:it!u 'f 1.E+4 :c .£. I- 0.1 "'ffi CJ1.E+3 .!:2 .Q c. "'C 0 --+ ~ 1.E+2 l -X- 20/1/97 PM, v*=1.87 0 X 20/1/97 PM ..-+ •• U) X 1.E+1 xX ··· ·XX t -+-312/97 PM, v*=1.75 e(I) +3/2/97 PM <( 06/4/97 PM ,,,,..0- Oo- - I 1.E+0 ½ -0-6/4/97 PM, v*=1.91 (j 0 0.01 1.E-1 0 0.2 0.4 0.6 0.8 1 0.1 1 10 Wavelength, ')..,(µm) r, radius (µm) Figure 5.21 :(a) Measured aerosol optical thickness, 'taen plotted as a function of wavelength, A(µm), for the afternoon of January 20, 1997, February 3, 1997, and April 6, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) is the aerosol size distribution calculated for the same days as (a) plotted as function of radius, r (µm). Chapter 5 Size Distribution ofAerosol Particles 151 the optical thickness did not fully increase with wavelength. It only increased for the first two values and remained almost unchanged for the rest of measurements. The resultant inversion was bimodal aerosol distribution. As in cases 2 and 4, inversion of this case was not easy and needed to be executed over a different size rang. Figure 5.22 for the afternoon of January 4, and June 16 1997, and Figure 5.23 for the afternoon of June 20 and July 2 1997, represent a type of aerosol that was rarely observed, where the inverted aerosol size distribution was bimodal with a coarse mode of rm = 2.0 µm. In all measured optical thickness of this case, a was positive, however, in Figure 5.22, the measurements produced a positive curvature, while in Figure 5.23, it yielded a negative curvature, which resulted in a narrower coarse mode than the previous one. However, one should be careful interpreting these two cases, as we have already shown (Section 5.4.1), that the error associated with inversions of rm > 1.0 µm is high. Unimodal aerosol size distributions were found to be usually associated with negative or low Angstrom coefficient, a, or v*::; 3.4, and usually low aerosol optical thickness, 'taer::; 0.05 at 'A = 0.502 µm, while power-law aerosol distributions were usually associated with a larger coefficient, v* ~ 3.4, larger optical thickness, 'taer ~ 0.05. Bimodal aerosol distribution was mainly observed when Angstrom coefficient and aerosol optical thickness were high, however, it was also observed for lower values. The bimodal nature of aerosol distribution observed in Sydney area is generally consistent with the revealed chemical composition of the particles ( Chapter 3 and 4 ), where it was found that carbon comprises of 28 % of the aerosol particles, while inorganics, mainly sulfates, were 30 % of the total mass. The carbon particles are usually the main constituent for the fine mode of (0.10 - 0.20 µm), while the larger particles, usually generated in a gas to particle conversion, mainly sulfate, forms the greater part of the larger mode (0.60 - 0.80 µm). Experimental measurements, combined with theoretical calculations, suggest an average value of rm =0.22 µm, for black carbon particles observed in Sydney (Chapter 3). 5.5.2 Junge coefficient Junge coefficient is known to be very sensitive to the composition of the air mass, 1.E+9 (a) ~ 1.E+B ~ 4/1/97 PM, v*=3.22 :;; ,I t -0- (b) ~ 1.E+7 ui I Vl Q) C: 0.1 X N"1,E+6 ~ X u x ....xx 'E :c ~1.E+5 I- Q_ .... c6 Cl u --0-- ~ 1.E+4 :.:: .._ c. o--o---- 0 z 0 0.01 -o 1.E+3 0 Vl 04/1/97 PM ....0 I 1.E+2 Q) <: X 12/6/97 PM 1.E+1 0.001 1.E+0 0 0.2 0.4 0.6 0.8 1 0.1 1 10 Wavelength, 'A(µm) r, radius (µm) Figure 5.22:(a) Measured aerosol optical thickness, 'tac,, plotted as a function of wavelength, A(µm), for the afternoon of January 4, 1997, and June 12, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) is the aerosol size distribution calculated for the same days as (a) plotted as function ofradius, r (µm), ,I 1.E+8 (a) -:, 1.E+7 ., I ..,bi," cti 1.E+6 en Q) C: .ll:: (.) f 1.E+5 :c I- ~ 0.1 6>1.E+4 0.01 1.E+0 0 0.2 0.4 0.6 0.8 1 0.1 1 10 Wavelength, 'A(µm) r, radius (µm) Figure 5.23:(a) Measured aerosol optical thickness, 'taer, plotted as a function of wavelength, A(µm), for the afternoon of June 20, 1997, and July 2, 1997. The curve line is the regression fit of the calculated optical thickness using the inverted size distribution. (b) is the aerosol size distribution calculated for the same days as (a) plotted as function of radius, r (µm). Chapter 5 Size Distribution ofAerosol Particles 154 mainly due to the aging process of aerosol particles. Holben et al. ( 1996) have shown that the wavelength exponent is sensitive to the rapid aging process of fresh smoke from fires in Brazil. Figure 5.24 and Figure 5.25 are plots of Junge coefficient, v*, against the aerosol optical thickness, Taer, at A = 0.502 µm, as measured during the morning and the afternoon, respectively. The morning correlation was not so obvious, as measurements were scattered and uncorrelated. While for the afternoon, the fast growing Junge coefficient can be seen clearly for small optical thicknesses, and a tendency to stabilise for the larger values. An optical thickness of a value Taer = 0.04 (Figure 5.25) could be seen as an identifier of the aging process when particles stays long enough in the atmosphere to coagulate, and form larger particles. The effect of high relative humidity would make the choice of identifier more difficult, as it will modify the particle size, aside from aging process. This could be the reason for the scattered and uncorrelated values of the observed v* against Taer during the morning, which is usually associated with higher relative humidity than the afternoon. However, a more precise identifier could be obtained, if measurements were associated with a major activity, such as a biomass fire. Dubovik et al. (1998) obtained a growth tendency similar to the one obtained for the afternoon measurements during the Brazil biomass burning season, but with less scattered measurements. They distinguished an optical thickness of Taer (0.440 µm) = 0.4 as an identifier for the burning season. 5.5.3 Effect of relative humidity on aerosol size distribution To investigate the impact of relative humidity on the aerosol size distribution, dry aerosol optical thickness, Taer(dry), was calculated from measurements, Taelambient), as follows T (d ) = Tae,(ambient) (5.24) aer ry f(RH) where f(RH) is the effective growth factor, calculated as described in Chapter 4, RH is the measured relative humidity. Particle growth was assumed to be constant over the whole wavelength range. The dry size distribution was then calculated using dry aerosol optical thickness and compared to the ambient aerosol distribution. For this comparison, three different Chapter 5 Size Distribution ofAerosol Particles 155 5 ------. 4.5 X X r 4 xX )f( ~ 3.5 'E -~ 3 -=a., 2.5 0 (..) 2 X f 1.~ y = 0.1677Ln(x) + 3.2245 R2 = 0.0257 0.5 0 --...... _...... ,_._ ...... _ ...... __...... _ ...... ___ _ 0 0.05 0.1 0.15 0.2 0.25 Aerosol optical thickness, (-rae,) Figure 5.24: Calculated Junge coefficient, v*, versus the aerosol optical thickness, for the morning period. The curve is the regression fit of the data. 6-.....------ - 5 X ~ X ~ 'E 4 a., ·c3 -= 3 ~ (..) & 2 ~ 1 0 __...... _ ...... __ ...... ,. 0 0.05 0.1 0.15 0.2 0.25 Aerosol optical thickness, {-tae,) Figure 5.25: Calculated Junge coefficient, v*, versus the aerosol optical thickness, or the afternoon period. The curve is the regression fit of the data. Chapter 5 Size Distribution ofAerosol Particles 156 days were selected and are presented in Figures (5.26, 27, and 28). Figure 5.26(a) is a plot of the inverted dry and ambient aerosol size distribution, dN/dlogr, versus the radius, r, on the morning of February 28, 1997. The measured relative humidity was 83% which corresponded to a growth factor off(RH)= 1.6, while (b) is the same as (a) except for the distribution is the volume weighted size distribution, r3n(r) or dV/dlogr (µm 3 cm-2). The figure shows a typical inverted bimodal aerosol distribution, that when corrected for growth, reveals a uniform drop of the aerosol number concentration only. Distributions of ambient and dry particles on the afternoon of December 19 1996 with a growth factor off(RH) = 2.04, Figure 5.27, and the morning of August 22, 1997, with a growth factor off(RH) = 1.39, Figure 5.28, showed similar differences, where the particle growth will only result in a uniform change of the particle number concentration, regardless of the nature of the distribution. This behaviour is caused by the assumption of a particle growth that is independent of wavelengths, i.e. for all particle sizes. Experimental evidence has already shown that growth factors increases with increasing wavelength (Kotchenruther et al., 1999). Typically, particle size is expected to shift to a larger size as they grow, and hence, the optical thickness will increase more at larger wavelength. As it is known, different size ranges have different chemical composition, and hence different growth factors. Therefore, only a size selective growth factor will account for the particle growth at different sizes. Experimental measurements of the humidity-dependent water uptake by deposited aerosol samples, by (Hitzenberger et al., 1997), showed a strongly size selective growth pattern. Remer et al., (1997) corrected the particle size rather than particle number density, which resulted in a uniform growth of particle diameter. In general, we would expect a non-uniform change of particle size and number concentration, with the change of relative humidity, as shown by experimental measurements using the Tandem Differential Mobility Analyser (TDMA) (Stein et al., 1994). Changes of relative humidity will affect the particle size and density, as well as the number concentration, as certain species may or may not exist at certain relative humidity (Chapter 4, Section 4.4.1). Correction of the growth of particle size caused by water uptake is of a great importance when ambient measurements are correlated with the widely used dry measurements such as the ASASP-X Particle Size Spectrometer or nephelometer, and 1.E+8 ....------, 9.E+5 1.E+7 ~ (a) 8.E+5 (b) 1.E+6 I 7.E+5 1.E+5 6.E+5 "I~ 1.E+4 ~ -1E-ambient E MO -&-dry .£. 1.E+3 E 5.E+5 ... ~ ... , 1.E+2 8' 4.E+5 '$ ~ 1.E+1 "lJs 3.E+5 1.E+O -1E-ambient 2.E+5 1.E-1 -&-Series1 1.E-2 1.E+5 " 1.E-3 +-_ __._...._...._..._...... ,....._1--_...... ,_...,_...,_.....,.....,_"""""' O.E+O 0.1 10 0 0.5 1.5 2 2.5 3 r, radius (µm) r, radius (µm) Figure 5.26: (a) Plot of the inverted size distribution on the morning February 28, 1997, versus the particle radius, r. Ambient distribution is the inversion of the measurements, while the dry inversion is the obtained after correction being made to the measurements using f(RH) = 1.6. The relative humidity is RH= 83 %. (b) is the same as (a) but for the size distribution is the volume weighted aerosol size distribution, dV/dlogr (µni3cm·2). 1.E+8 ------, 8.E+5 1.E+7 7.E+5 (b) (a) 1.E+6 6.E+5 "' ~ 1.E+5 0E 5.E+5 -&-dry (.) ~ ME ~ambient 5, 1.E+4 ~ 4.E+5 .S2 ~ ~ g> ~ 1.E+3 ~ 3.E+5 "O 1.E+2 ~ambient 2.E+5 -&-dry 1.E+1 1.E+5 1.E+0 ~ _ __._...... ___._...... _...... _ ___._ ...... ______.._._...... , 0.E+0 10 0.1 0 0.5 1.5 2 2.5 3 r, radius (µm) r, radius (µm) Figure 5.27: (a) Plot of the inverted size distribution on the afternoon of December 19, 1996, versus the particle radius, r. Ambient distribution is the inversion of the measurements, while the dry inversion is the obtained after correction being made to the measurements using f(RH) = 2.04. The relative humidity is RH= 93 %. (b) is the same as (a) but for the size distribution is the volume weighted aerosol size distribution, dV/d/ogr (µm 3cm·2). 1.E+9 -..------, 8.E+5 ------, 1.E+S 1.E+7 7.E+5 1.E+6 (a) (b) 1.E+5 6.E+5 ,;-- 1.E+4 '1' -8-dry § 1.E+3 § 5.E+5 ME ~ambient - 1.E+2 _;, 4.E+5 g>1.E+1 g, ~ 1.E+0 ~ 3.E+5 ~ 1.E-1 :s 1.E-2 1:1 2.E+5 1.E-3 ~dry 1.E-4 -*-ant>ient 1.E+5 1.E-5 1.E-6 +--~-~~~~-+----...... ,-~~-1 0.E+0 ,- I w: Ii II I II I II I II ' 1 10 0.1 0 0.5 1.5 2 r, radius (µm) r, radius (µm) Figure 5.28: (a) Plot of the inverted size distribution on the morning of August 22, 1997, versus the particle radius, r. Ambient distribution is the inversion of the measurements, while the dry inversion is the obtained after correction being made to the measurements using t{RH) = 1.39. The relative humidity is RH= 74 %. (b) is the same as (a) but for the size distribution is the volume weighted aerosol size distribution, dV/dlogr (µm 3cm·2;. Chapter 5 Size Distribution ofAerosol Particles 160 when used in radiative forcing models. However, to model such growth accurately, size resolved chemistry is required to improve the quality of correction, and produce a more accurate size resolved growth factor. In this work, we tried to simplify the problem by using a particle growth factor to correct for the spectral optical thickness values, assuming it is constant over the whole wavelength range. Although this approach may not provide a complete answer to the problem, it provides a good insight to the difficulties of such task, and the need of more experimental work to model such effects. 5.6 Summary and conclusion The problem of using inversion methods to determine the columnar aerosol particles size distribution from aerosol optical thicknesses measurements was investigated using two different techniques, constrained linear and analytic eigenfunction theory. It was demonstrated that for such limited spectral range of measurement for an instrument as the MFRSR (A= 0.415, 0.502, 0.616, 0.673, 0.870 µm), the constrained linear inversion was the best option to describe the bimodal distribution that is usually observed in Sydney. The aerosol size distribution of Sydney was determined by inversion of MFRSR optical measurements. It was found that the bimodal distribution was the dominant aerosol distribution type that was observed, comprising 82 % of the data examined, while the unimodal distribution was observed occasionally, in nearly 10.5 % of the data. The observed power-law distribution type was only 7.5 % of the data. Mode radii of the bimodal aerosol were rm = (0.10 - 0.35) µm and rm = (0.6 - 0.85) µm, while it was rm = (0.10 - 0.35) for the unimodal distribution. Most of observed unimodal and power-law aerosol distributions occurred at the morning period, during the winter season, (July, August and September). The retrieved unimodal distribution was usually associated with a Junge coefficient of v* ~ 3.4, and low aerosol optical thickness, Taer~ 0.05 at A= 0.502 µm. While a power-law aerosol distribution was found to be associated with larger coefficient, v* ~ 3.4, and larger optical thickness values, Taer ~ 0.05. On the other hand, bimodal aerosol distribution was not associated with either low or high values of Junge coefficient, or optical thickness; nonetheless, it was mainly observed when the Angstrom coefficient and aerosol optical thickness were high. Chapter 5 Size Distribution ofAerosol Particles 161 Effects of particle hygroscopic growth, caused by relative humidity, were studied using the calculated growth factor. Dry optical thickness values were calculated, and then inverted to obtain a dry size distribution. It was shown that a more accurate size resolved growth factor is required to account for particle growth at different sizes. Particle size growth studies are vital to the improvement of the quality of radiative forcing models, and the cross correlation with the more popular dry size distribution instruments. Chapter 6 Determination Of Index Of Absorption Using MFRSR 6.1 Introduction As we have discussed in more detail in Chapter 3, atmospheric aerosols are a significant contributor to the absorption of solar radiation, as well as the scattering process. However, most of the light scattered by particles will be redirected as diffuse light. A careful study of such diffuse light would provide some information about the absorption of aerosol particles and the surface reflectivity. Herman et al. ( 197 5) suggested in a preliminary study that the ratio of the diffuse and direct solar radiation at the Earth's surface could be used to determine the complex part of the index of refraction. King and Herman (1979), developed a statistical technique to infer the values of ground albedo and index of absorption, by comparing measurements of the ratio of diffuse to direct solar flux with radiative transfer computations of the same ratio. They successfully applied this technique to hemispherical radiometer measurements, to determine the ground albedo and index of absorption in Tucson, US (King, 1979). In this chapter, we will investigate the use of MFRSR measurements of the diffuse and direct solar radiation at wavelength A = 502 nm, in order to determine the imaginary part of the refractive index of atmospheric aerosol and surface reflectivity. We will also present the required theoretical prediction of the ratio, using a radiative transfer model. The technique and the theoretical prediction of the diffuse to direct ratio will be discussed briefly in the following sections. Chapter 6 Determination ofIndex ofAbsorption .. 163 6.2 Theoretical calculation 6.2.1 Diffuse to Direct method Chandrasekhar (1960) described the diffuse reflection and transmission as the most fundamental problem in the theory of radiative transfer in plane-parallel atmospheres. In Figure 6.1, the incident parallel beam of radiation, with net flux of F0 per unit area normal to itself, at a zenith angle, 00 , and azimuth angle, (/)0 , reaches a plane-parallel atmosphere of optical thickness r = 'li- The direct normal transmitted flux is given by F=Fe-r,Iµ. 0 (6.1) where In the case of zero ground reflectivity, both the intensity diffusely reflected from the surface r = 0, and the intensity diffusely transmitted below the surface r = 'li, can be expressed in terms of the reflection and transmission functions as F 1(0,+µ,(/J) = - 0 S(r,;µ,(/J;µo,'Po) (6.2) 4:tµ and (6.3) where S(r,;µ, J(r1 ,-µ,(/J) here represents the diffusely transmitted intensity, which does not Chapter 6 Determination ofIndex ofAbsorption .. 164 l(O, +µ,(/J) l(Tt,-µ,(/J) Figure 6.1: The standard case of diffuse reflection and transmission by a plane parallel atmosphere. Chapter 6 Determination ofIndex ofAbsorption .. 165 include the directly transmitted solar intensity F,, e --r, 1µ. . It represents the reduced incident radiation, which penetrates to the level 'tt without suffering any extinction process. This was the solution of a standard problem. However, in a real situation, the laws of diffuse reflection and diffuse transmission will have to be modified by the presence of ground reflectivity, A. Chandrasekhar (1960) defined the reflections, also known as local or planetary albedo, and (diffuse) transmission t associated with reflected (upward) and transmitted ( downward) flux densities as (6.4) (6.5) The spherical or global albedo, which represents the ratio of the energy reflected by the entire planet or spherical atmosphere to the energy incident on it when the surface reflectivity is zero, is (6.6) In a similar way, the global diffuse transmission is (6.7) and we may also define the total transmittance as (6.8) Chandrasekhar (1960) gave a solution to what he described as the planetary problem, by considering the case of scattering according to a phase function. The ground is assumed to reflect according to Lambert's law, therefore the upward intensity at r = 'ti will be the same in all outward directions, and is Chapter 6 Determination ofIndex ofAbsorption .. 166 called lg. The outward normal flux = 1lfg = A x the inward normal flux. The total intensity that reaches the ground is given by 1(1:,.-µ, Fe-T,/µo 0 ' The diffusely transmitted intensity F0 t(µ), and the flux that comes from the reflection of lg by the atmosphere, ,r lg s( 1:1 ). Combining the three components of flux density yields the following expression for the total inward intensity or the diffuse radiance received at ground level The downward diffuse flux density, F(Tt), can be derived using the above equations as or (6.11) King and Herman (1979) have shown that the ratio of the diffuse flux density to the directly transmitted solar flux density, (6.13) or (6.14) From the above equation, the diffuse to direct ratio is dependent on ground reflectivity or albedo, optical depth, and solar zenith angle. It is also dependent on the refractive index and size distribution through the transmittance and spherical albedo functions, t(Tt,JJ,o) and s( r,). These functions can be calculated by solving the radiative transfer equation for different values of refractive index and size distribution, assuming zero ground reflectivity. Herman et al. (1975) proposed a method for the determination of the complex refractive index of atmospheric aerosol by comparison of measurements of the ratio of diffuse to direct solar radiation with theoretical calculations of the same ratio. They studied the sensitivity of the ratio to refractive index and concluded that it is highly sensitive only to the complex part of refractive index. The effect of the real part was minimal. 6.2.2 Radiative transfer equation The basic equation for the problem of multiple scattering in a plane-parallel atmosphere is given by (Liou, 1980) µ dl(~:, Lenoble, 1985) dl(r,µ, J(r,µ, Herman and Browning (1965) presented a reliable numerical solution to the radiative transfer equation, in which three integrations over azimuth angle, zenith angle and optical depth, are computed numerically with a crude integration increment over azimuth: eg. 30°. Dave and Gazdag (1970) showed that multiple scattering calculations can be simplified, using a modified form of the Fourier series, which make the calculation even simpler for fluxes, which are computed by averaging intensities of the emerging radiation over azimuth angles. 6.2.3 Radiative characteristics The definition of the complex scattering functions, SI(x,m, 0) and S2(x,m, 0), which are related to the amplitude of the scattered radiation, is given by (Liou, 1980) (6.18) and (6.19) Chapter 6 Determination ofIndex ofAbsorption .. 169 where 1 1r1(cos0) = -.-P/(cos0) sm0 d i"i(cos0) = -P/(cos0) d0 ai(x,m) and b1(x,m) are Mie coefficients (Chapter 3), x is the size parameter, m is the refractive index, and 0 is the direction of scattered radiation, measured from the forward direction. Afterward, Mie theory is used to calculate the extinction ( e), scattering (s) and absorbing (a) efficiency factors, Qx, x = e, s, or a, for a specific refractive index, as described in Chapter 3. 6.2.3.1 Phase function and Asymmetry parameter The normalized angular distribution of the scattered energy for a single sphere, or the phase function, is defined as (Vermote et al., 1997) 2 p(A,r,m,0)= ( )2 ( )[s,(x,m,0)s;(x,m,0)+s2 (x,m,0)S;(x,m,0)](6.20) x/r Cs A,r,m where Cs(A,r,m) is the scattering cross section (Chapter 3), and r is the particle radius. Another important parameter needed to be calculated from the scattering amplitudes is the average cosine of the scattering angle, or the asymmetry parameter g, a measure of the forwardness of the phase function, and is defmed as I fcostp(0;m,A)d cos0 g = --'-,------fp( 0; m, A )d cos 0 -1 or Chapter 6 Determination ofIndex ofAbsorption .. 170 2 g = -i-I(l(l + ) Re(a1(x,m)a;+i (x,m) + b1(x,m)b;+ 1 (x,m)) + X Qs I f + 1 (6.21) 21 + 1 ( • )) ( ) Re a1(x,m)b1 (x,m) ll+l For a particle that scatters light isotropically, (the same in all directions), or if the scattering is symmetric about the scattering angle of 0 = 90°, then g vanishes. If particles scatter more toward the forward ( 0 = 0°), then g is positive. It is negative if the scattering is more toward the back direction ( 0 = 180°) (Bohren and Huffman, 1983). 6.2.3.2 Aerosol size distribution Two different aerosol size distribution models were used in the current calculation. The first is the Junge power-law distribution described by dN(r) = c(r0 )a dr r with a is between 3 and 5, and c is the number density of particles with radius r0 • The second model is the inverted aerosol size distribution, described in Chapter 5, where dV(r)/dlogr =2.3xr4dN(r)ldr. It follows that, for a given refractive index, and using Mie calculation, the extinction (e), scattering (s) and absorbing (a) optical thickness is calculated as The single scattering albedo, m, which defines the fraction of the energy removed from the incident beam that will reappear as scattered radiation, is defined as Chapter 6 Determination ofIndex ofAbsorption .. 171 (6.22) Here tn(A,m) is the column single scattering albedo. 6.2.4 Determination of index of absorption and ground reflectivity King and Herman (1979) proposed a statistical method for the determination of the index of absorption, K, and ground reflectivity, A, based on comparing experimentally determined diffuse to direct ratio of solar radiation with a theoretical calculation of the ratio. A functional form of the ratio, Equation (6.14), can be written as (6.23) where Assuming that c(K)A «l, then the above equation can be simplified into a linear function of A, with an intercept oft( r 0 µ)e-T,fµ and a slope ofµ 0 s( T1) as The maximum probability that the measured diffuse to direct ratio, ;, has the functional form of the calculated ratio, <1>(0;,K, A), is equivalent to minimising the statistic x2 by making unweighted least-square fit to the data. Chapter 6 Determination ofIndex ofAbsorption .. 172 x 2 = I[; -<1>(0;,K,A)J2 i 0; is the solar zenith angle of the measurements. The minimum value of X2 is achieved by the following two conditions (6.24) (6.25) Then A can be calculated from (6.26) To solve the above problem, a(00 ,K) and b(00 ,K) are calculated theoretically for different values of imaginary refractive index K. Then A and x2 are computed using the above equations. K is altered to reach a minimum x2, whereas A is calculated for a minimum X2 at a given K. 6.3 Results and discussion 6.3.1 Theoretical calculations In order to calculate the ratio of diffuse to direct <1>(0;, K, A), using a radiative transfer model, aerosol radiative characteristics needed to be determined. The 6S model (Vermote et al., 1997) is used to calculate the single scattering albedo (i](A,m), phase function p(A,r,m, 0), and asymmetry parameter, g, for different complex refractive indices. The input of the model was the aerosol size distribution in the form of either Junge distribution, or dV/dlogr, calculated using the aerosol optical thickness measurements, as described in Chapter 6 Determination ofIndex ofAbsorption .. 173 Chapter 5. The advantage of using the Junge distribution is that it will account for the particles of very small radii, < 0.1 µm, which are significant contributor to absorption processes (Chapter 3). Nonetheless, the choice of what aerosol size distribution model to use was not critical for the final outcome. Figure 6.2 is a plot of the phase function as a function of the scattering angle, computed by 6s model, for April 30 1997, at wavelength 11, = 0.515 µm, using two values of index of absorption, K = 0.0 and K = 0.03. Single scattering albedo are UJ = 1.0 and 0.773, while the asymmetry parameters are g = 0.671 and 0.728 respectively. The next step is to calculate the spherical albedo, s( r 1 ), and the transmission function, t(Tt,/J,o), for different solar zenith angles, 00 , and different index of absorption, K. We used the radiative transfer model GAUSEID, which uses the Gauss-Seidel iterative method of Herman and Browning (Herman and Browning, 1965), using the algorithm of J. V. Dave (Dave and Gazdag, 1970), to compute the intensity of the radiation emerging at any level of a plane-parallel atmosphere containing arbitrary known vertical distributions of molecules, ozone and aerosols, and bounded at the bottom by a Lambertian surface. The previously determined total, Rayleigh, and ozone optical thicknesses, along with the calculated asymmetry parameter, scattering and absorption aerosol optical thicknesses, were the input to the model. Figure 6.3 is the plot of the atmospheric transmission function t(Tt,/J,o), as a function of cosine of the solar zenith angle, f.lo, calculated for the afternoon of 30 April, 1997 (Taer =0.0298, and a=3.65). The function was plotted for different index of absorption, K, at wavelength A = 502 nm. The plot shows a clear dependence of the transmission function on the solar zenith angle, and to a lesser extent on the index of absorption. The spherical albedo, s( -r 1 ) , is only Chapter 6 Determination ofIndex ofAbsorption .. 174 1 . E +03 --...... -+---.- ...... ,...... ,.--+-.....--,...... ,...... ,...+-,---,--- ------K=O.O, UJ=l.O c: 1.E+02 0 t5 --K=0.03, m=0.773 C: ir 1.E+01 Q) en ea .c: a.. 1.E+00 1.E-01 0 50 100 150 Scattering Angle (degree) Figure 6.2: Phase Function of aerosol particle at wavelength A = 0.515 µm, calculated for the afternoon of April 30 1997. Index of absorption is K = 0.0 for the dotted line and K = 0.03 for the continuous line. Chapter 6 Determination ofIndex ofAbsorption .. 175 0. 1 0 +-,--,-,...... ,...-t-,---r-,---,-+--r--r-T-i-+--r-.,...... ,....,....-11--r-.,...... ,.....;---,-.,...... ,...--r-+ 0.08 0.06 'l ~ -+-1.53-0.0i, Sbar=0.1241 -..... 0.04 -1.53-0.005i, Sbar=0.1219 -e-1.53-0.01i, Sbar=0.1207 0.02 1.53-0.03i, Sbar=0.1179 0. 00 --+---"...... +-'- ...... _.._+--"-...... +-'- ...... ,...... _ ...... ,_.._..__._~ 0.00 0.20 0.40 0.60 0.80 1.00 1.20 Figure 6.3: Atmospheric transmission function t(ri,µJ, vs. cosine of the solar zenith angle, µ0 as calculated for the afternoon of30 April, 1997. Chapter 6 Determination ofIndex ofAbsorption .. 176 a function of the optical thickness, and hence the 1magmary part of the refractive index. The computed diffuse to direct ratio <1>(0;,K,A) was determined using Equation (6.14). Figure 6.4 (a, b, c, and d) are plots of the modelled ratio, , for a reflectivity of A = 0.05 and 0.30, as a function of the solar zenith angle, 00 , calculated for the afternoon of 30 April, 1997. in Figure 6.4 (a, b, c, and d) is computed for a complex refractive index of K = 0.0, 0.005, 0.01 and 0.03 respectively. The plots illustrate the sensitivity of the ratio to the zenith angle, 00 , and the index of absorption K, as well as the ground reflectivity A. Since theoretical calculation of the ratio can be determined as a linear function of A, only two values of ground albedo are required, A = 0.05 and 0.30, for any given refractive index. The previous figures show a smooth ratio, , as a function of 00 , K and A, which means that an interpolation will be possible to predict for other values. Therefore, only four values of refractive index (K = 0.0, 0.005, 0.01, and 0.03), at seven zenith angles (00 = 15-t75) are required. Spline under tension interpolation (Cline, 1974) is used for the interpolation method. 6.3.2 Experimental results The measured diffuse to direct ratio meas was determined using measurements of the diffuse, Fm~as( r, ), and global flux density, Gmeal'l't), for a measured solar zenith angle, 00 as (6.27) Figure 6.5 is a plot of meas versus 80 , measured using the MFRSR in Sydney, at wavelength of A =502 nm, for 30 April 1997. This day was cloud free, with fairly stable meas ratio that begins increasing at zenith angle 00 = 70°. The morning is less stable than the afternoon, as the ratio increases with Chapter 6 Determination ofIndex ofAbsorption .. 177 0.16 +-...... -+-, ...... -+-, ...... -+-, ...... ,...... 0.15 0.14 --&- 0.11 0.1 +-._...... _ ...... _ ...... ______...... _____+ 0 20 40 60 80 8, Degree Figure 6.4 (a): Calculated diffuse-direct ratio for 30 of April, 1997 afternoon ( 'l"ae,= 0. 0298, a= 3. 65), for a ground reflectivity A = 0.05 & 0.3 and m = 1.53-0.0i. The spherical albedo is S = 0.1241. 0.16 0.15 0.14 & 0.13 --&- 0.11 0.1 ._...... _ ...... _ ...... _ ...... + 0 20 40 60 80 8, Degree Figure 6.4 (b ): Calculated diffuse-direct ratio for 30 of April, 1997 afternoon ( 'l"ae, = 0. 0298, a= 3. 65), for a ground reflectivity A = 0.05 & 0.3 and m = 1.53-0.005i. The spherical albedo is S = 0.1219 Chapter 6 Determination ofIndex ofAbsorption .. 178 0.16 0.15 0.14 e 0.13 ~ 0.11 0.1 0 20 40 60 80 e, Degree Figure 6.4 (c): Calculated diffuse-direct ratio for 30 of April, 1997 afternoon ( Tae, = 0. 0298, a= 3. 65), for a ground reflectivity A = 0.05 & 0.3 and m = 1.53-0.0li. The spherical albedo is S = 0.1207 0.16 +-...... -.-....- ...... -.-..-...... + ...... _,,...... ,....,...... 0.15 0.14 e 0.13 ~ 0.11 0.1 +-...... +---- ...... ---.i.....+...... --- ...... -+ ...... -+ 0 20 40 60 80 e, Degree Figure 6.4 ( d): Calculated diffuse-direct ratio for 30 of April, 1997 afternoon ( Taer = 0.0298, a= 3.65), for a ground reflectivity A = 0.05 & 0.3 and m = l.53-0.03i. The spherical albedo is S=0.1179. Chapter 6 Determination ofIndex ofAbsorption .. 179 0.45 i 0.40 - X 0.35 0 0 0.30 * oPM X o> "' 0.25 X AM "'Q) E J ,e. 0.20 0.15 0.10 0.05 0.00 45 55 65 75 85 95 80, Degree Figure 6.5: Diffuse to direct ratio, zenith angle throughout the day. This behaviour of the ratio meas for the morning is not consistent with the theoretical prediction shown in Figure 6.4, where ratio slightly decreases with 00 , and increases for larger solar angle. The reason for this inconsistency with the theory could be due to the higher aerosol loading throughout the morning than the afternoon, which was more stable ( 1"ae,.(PM) = 0.0298, a= 3.65, and Taer(AM) = 0.0963, a= 4.27). This change in aerosol loading is critical, as theoretical calculation of the diffuse to direct ratio assumes a stable atmosphere during the day. Effects of this behaviour on the solution will be discussed later. To obtain the complex index of absorption of atmospheric aerosol particles from the continuous measurements of diffuse and global flux densities of the MFRSR at wavelength A = 502 nm, we applied the statistical method to the measurements, as outlined in the previous section. We have selected six cloud-free days during 1997, to be closely investigated. Summary of the results is presented in Table 6.1, which includes the previously determined aerosol optical thickness, Z"aen and Junge coefficient, a= v*+ 1, the inferred index of absorption, K, and ground albedo, A. Single scattering albedo, m, and asymmetry factor, g, were subsequently calculated using the inferred complex refractive index. Out of the nine sets of data, only one third of the data under study were successful. While for the rest, the technique produced a negative surface albedo for any value of K, which is physically unrealistic. Still, the resultant values of complex refractive index, K, were reasonable. It is obvious that for this case the theoretical calculation overestimated the measurements. This implies that some of the theoretical parameterisations are not accurate, possibly caused by the fluctuation of the aerosol optical thickness during the day. On the 30 of April 1997, a successful solution was possible only for the afternoon, but not for the morning period. As was discussed earlier, in Figure 6.5, unlike the morning, the afternoon measured diffuse to direct ratio, meas, followed a similar behaviour to the theoretical calculation, while in the Chapter 6 Determination ofIndex ofAbsorption .. 181 Table 6.1: Summary of the obtained complex refractive index K, and reflectivity A, for a measured 'taeu and a.. Single scattering albedo, tiJ, and asymmetry factor, g, were calculated using the obtained refractive index K. DATE AMI K A Taer a=v*+J g PM 20/01/97 PM 0.0355 0.0288 3.33 0.694 0.7673 22/02/97 AM 0.0035 0.091 0.1671 4.10 0.9813 0.6636 PM 0.0488 0.081 0.1291 3.59 0.7073 0.7472 11/03/97 AM 0.0382 0.0497 3.68 0.7385 0.7335 PM 0.0345 0.0413 3.26 0.6835 0.7741 30/04/97 AM 0.0423 0.0963 4.27 0.7665 0.6855 PM 0.0384 0.245 0.0298 3.65 0.7334 0.7366 13/05/97 PM 0.0477 0.0311 5.1 0.7454 0.6314 28/07/97 PM 0.0427 0.0351 3.92 0.7573 0.6309 Average 0.0369 0.139 0.7362 0.696 s.d (cr) 0.0135 0.092 0.0281 0.0592 Chapter 6 Determination ofIndex ofAbsorption .. 182 morning, the instability of atmosphere contributed to the unphysical value of the reflectivity, since theoretical model is based on the assumption of a constant aerosol optical thickness during the day. King (1979) reported similar cases where the aerosol optical thickness fluctuated during the day, and no possible solution could be obtained. To achieve such solution, it was necessary to measure Another approach was to use a constant, more realistic, value of ground albedo, A = 0.12, 0.16, and 0.30, and calculate the index of absorption. The value used for ground albedo was not crucial and had minimal impact on the final value of the inferred complex refractive index. Figure 6.6 is a plot of the observed and computed diffuse to direct ratio, , versus the solar zenith angle, 00 , for April 30, 1997. This day represented a successful attempt to infer both the aerosol index of absorption, K, and the ground albedo, A, using the fitting procedure as described in the previous section. The result was K= 0.0386, and A = 0.245. Figure 6.7 is the same as Figure 6.5 but for 22 February 1997. The plot shows a reasonably stable atmosphere, with some cloudy breaks during the day, which were not included in the final solution. Observed aerosol optical thicknesses on the day were high, (taer = 0.167 and a= 4.1) for the morning while it was lower ( 'Z"aer= 0.129 and a= 3.59) for the afternoon. Figure 6.8 and Figure 6.9 present a plot of the resultant computed diffuse to direct ratio compared the measured ratio for the morning and the afternoon respectively. The technique was applied successfully, and the inferred complex refractive index was K = 0.0035 for the morning and K = 0.0488 for the afternoon. Ground albedo was almost unchanged during the day, as it was A= 0.091 and A= 0.081 for the morning and afternoon respectively. The obtained average complex refractive index was K= 0.0369, with standard deviation of o-=0.013 5. The average single scattering albedo was tIJ = 0. 736, with o- =0. 0281. All the inferred values of complex refractive index showed a reasonable consistency and stability, aside from one value of K= 0.0035. The inferred values varied from K= 0.0345~0.0477, with an average close to the value determined on 30 April, 1997. This was consistent with the chemical composition analyses, and dry absorption measurements of the LIPM, which detected elemental carbon that comprised 30 % of aerosol particles in the Sydney region (see Chapters 3 and 4). It is obvious that to apply the diffuse to direct technique successfully, a Chapter 6 Determination ofIndex ofAbsorption .. 184 0.14 0.12 0.10 ~ 0.08 ,e. 0.06 0.04 -COMPUTED o MEASURED 0.02 0.00 40 50 60 70 80 00 , degree Figure 6.6: Measured and computed diffuse to direct ratio as a function of solar zenith angle, at wavelength A = 502 nm, on the afternoon of30 April 1997. The inferred complex refractive index, K = 0.0384, and the ground reflectivity, A = 0.245. Chapter 6 Determination ofIndex ofAbsorption .. 185 1.40 X 1.20 oPM X 1.00 xAM ~ rt) I'll 0.80 Q) ~ E '9 0.60 0.40 0.20 0.00 20 40 60 80 100 eo, Degree Figure 6.7: Diffuse to direct ratio, 1Pmeas, for the morning and afternoon of 22 of February 1997, at wavelength A = 502 nm. Chapter 6 Determination ofIndex ofAbsorption .. 186 0 .40 __ ....,....,..-,-...... ,_,,...... ,...... ,...... + ...... - ...... -- 0. 35 0.30 rJ) 0.25 (0 ~ 0.20 o.15 e -COMPUTED 0.10 o MEASURED 0.05 0.00 ...,...... __...... __...... __...... __...... _ 50 55 60 65 70 75 00 , degree Figure 6.8: Measured and computed diffuse to direct ratio as a function of solar zenith angle, at wavelength ').. = 502 nm, for the morning of February 1997. The inferred complex refractive index, K = 0.0035, and the ground reflectivity, A= 0.091. 0.40 0.35 0.30 0.25 rJ) (0 Q) 0.20 eE 0.15 0.10 -COMPUTED 0.05 o MEASURED 0.00 20 30 40 50 60 80 , degree Figure 6.9: Measured and computed diffuse to direct ratio as a function of solar zenith angle, at wavelength')..= 502 nm, for the afternoon of February 1997. The inferred complex refractive index, K = 0.0488, and the ground reflectivity, A= 0.081. Chapter 6 Determination ofIndex ofAbsorption .. 187 stable atmosphere during the measurement period is required, or more sophisticated theoretical calculations will be needed to accommodate the unstable conditions of the atmosphere, including the fluctuations of the aerosol optical depth during the day. These conditions are not normally met in Sydney. Under the circumstances, we believe that the results we obtained, though far from perfect, are encouraging, and provide some valuable information about the ambient aerosol complex refractive index and single scattering albedo. However, better results could be achieved by investigating more cases, which can satisfy the theoretical predictions, a job that might be time consuming, is possible, and could provide better understanding of the aerosol absorption. Also radiative transfer formulation needed to be developed, so as to handle the varying conditions of the atmosphere. 6.4 Summary and conclusion The MFRSR measurements of diffuse to direct ratio, , were compared to the theoretically calculated ratio, to infer the complex refractive index of aerosol particles and ground reflectivity or surface albedo. The outcome was encouraging for the index of absorption, and yielded an average of K= 0.0369, with standard deviation of a=0.0135. The average single scattering albedo is UJ = 0. 7362, with a= 0.0281. As for the ground reflectivity, it failed to produce a realistic value for the majority of the days investigated. The average value of the successfully inferred reflectivity was A= 0.14. For the future, the inclusion of more days to be investigated, a much improved theoretical calculation, and the breakup of the day into several stable periods, though time consuming, should improve the solution. The availability of other independent observations such as ground reflectivity would provide some useful information that will reduce the problem into solving for the unknown index of absorption only. The availability of continuous in situ absorption measurements to be compared against the MFRSR measurements is necessary to obtain more accurate test of closure or consistency study between Chapter 6 Determination ofIndex ofAbsorption .. 188 the two methods, as well as the models that link them. Continuous absorption measurements, alongside nephelometer scattering measurements, can be used to estimate a value of single scattering albedo m. Chapter 7 Inferring Total Ozone And Aerosol Optical Thickness Using Eigenvalue Analysis 7.1 Introduction The determination of aerosol optical thickness values from spectral radiometer measurements of total optical depth or thickness is a straightforward procedure in regions not affected by trace gases, since the only other component is the molecular or Rayleigh scattering, which can be easily calculated as a function of wavelength, given the surface pressure. However, for measurements in spectral regions that are sensitive to gaseous absorption, more information about that contribution is required. Measurements made at 0.502, 0.615 and 0.673µm, by the Multifilter Rotating Shadowband Radiometer (MFRSR), are also affected by ozone. In the absence of independent measurements of the total ozone column, a way of estimating the ozone is essential to retrieve an accurate aerosol optical thickness. One way of solving the problem is to predict the aerosol optical thickness at these channels, using measurements outside this region, and subtract the aerosol contribution from the measured optical thickness, to give the total ozone. Flittner et al. (1993) used a second-derivative smoothing technique to infer the total ozone, and compared the result with the ozone determined using the King and Byrne (1976) method. They compared both measurements to TOMS values for the same dates, and found that both agreed to within 15 %, with a slight advantage to King's method, 12 %. They concluded that improvement to this technique could be achieved by combining measurements from both the Chappuis and Huggins ozone absorption bands, that is by adding independent information to the set of already existing measurements. In this chapter we present a new method for predicting the aerosol optical thickness and thus inferring total ozone. In the next section we describe the method, outlined by Box et al. (1996), which is based on eigenvalue analysis. Both the new method of eigenanalysis and King's technique were applied to the data collected in Sydney using a MFRSR. Results of the inferred ozone column are compared with Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 190 both Earth Probe (EP) and ADEOS Total Ozone Mapping Spectroradiometer (TOMS) overpass measurements. 7.2 Theory 7 .2.1 Determination of ozone optical thickness and the prediction of 0.614 µm measurements The total optical thickness 'Z(A) is obtained using the Lambert-Beer law. The aerosol optical thickness, 'Z'"aer(A), is calculated by removing the molecular or Rayleigh optical thickness, 't'R(A), and the ozone optical thickness, r03(A), contributions, (7.1) where the ozone optical thickness is a function of the total ozone column, measured in Dobson units, and can be expressed as: (7.2) where aJ is the spectral absorption coefficient for ozone, and U03 is the total ozone column. In the absence of independent ground-based measurements of total ozone, both the eigenvalue analysis technique and King's method were used to predict the aerosol optical thickness of the contaminated channels, and subsequently infer total ozone. 7 .2.2 Eigenvalue analysis The measured aerosol optical thickness is related to the aerosol size distribution by the following integral equation, raer(J) = [ 1rr2Q(r,J,m)n(r)dr (7.3) where 't'aer(A) is the aerosol optical thickness from Equation (7 .1) at wavelength A, Q is the Mie extinction efficiency factor, m is the complex refractive index, r is the particle radius and n(r) is the number of particles per unit area per unit radius in a vertical column through the atmosphere, with radii in the size range between rand r + dr. We may write the generic form of this Fredholm integral equation of the first kind as Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 191 b gi = fK;(x)f(x)dx+ c; i = 1,2 .... N (7.4) a where Klx) is the kernel function, g; is the set of measurements, c; are the measurement errors, and f(x) is the unknown function we wish to retrieve. For convergence, the kernel K to be used here is given by K = Q(r,J,m) (7.5)) r and g; is equivalent to T(AJ, andf(x) is equivalent to m-3n(r) (x = r). The kernels of this equation are not independent of one another (Twomey, 1974; 1977), meaning that N measurements do not necessarily yield N independent pieces of information. Often, one measurement can be predicted as a linear combination of the others, within the limits of measurement error. Eigenvalue analysis provides a means of making these predictions. To perform such an analysis, we need to construct a kernel covariance matrix which contains the wavelength information for the channels to be used as predictors and that to be predicted, in our case the MFRSR channels. The aerosol refractive index for each wavelength, and various types of aerosol, were taken from d' Almeida et al. (1991), and are presented in Table 7.1. The kernel covariance matrix is written as C=(Cu) (7.6) Where or in a matrix form of Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 192 Table 7.1: Wavelengths and refractive indices for MFRSR set of wavelength, of various types of aerosol. Aerosol Type A(mn) Average Urban Dust-like Continental 414.4 1.543-0.033i 1.615-0.189i l .53-0.008i 501.7 1.543-0.033i 1.615-0.185i 1.53-0.008i 615.5 l .543-0.033i l.615-0.180i l .53-0.008i 672.7 l .543-0.033i 1.615-0.180i 1.53-0.008i 869.8 1.534-0.037i 1.615-0.182i 1 .52-0.008i 933.6 l .535-0.039i 1.616-0.185i l .52-0.008i Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 193 The eigenvalues and eigenvectors of this matrix are found. The relative values of the eigenvalues give us information about the number of independent pieces of information in the measurements. Here we are more interested in the eigenvectors. 7 .2.2.1 Prediction of 0.615 measurements The aerosol measurement at 0.615 µm, gm, can be calculated from other measurements using the procedure outlined by Twomey (1977, section 8.4). For any eigenvalue that is sufficiently small, we can write n I>;K;(x)=O i=l where ai are the components of the eigenvector corresponding to that eigenvalue. This means that we can express a particular kernel as a linear function of the rest so that Km= °Ib1K1 (x) (7.7) j,t.m where bj = - aj I am. It therefore follows that gm is calculated directly from the other wavelengths using gm= Lb1 fK 1 (x)f(x)dx, J,t.m or simply by using (7.8) The coefficients, bj, were calculated for 3 different types of aerosol, using the eigenvector associated with the smallest eigenvalue. Values of these coefficients are given in Table 7.2. Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 194 Table 7.2: Coefficients used for prediction of0.615 µm measurements for MFRSR wavelengths. Wavelength, µm Aerosol Type 0.414 0.502 0.673 0.870 Average Continental -0.067 0.314 0.879 -0.130 Urban -0.065 0.310 0.875 -0.123 Dust-like -0.070 0.315 0.887 -0.137 Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 195 To test the applicability of this approach to predicting the aerosol contribution to the ozone channel, some simulation studies were done. Four different lognormal distributions were chosen representing narrow and medium width distributions with mode radii at 0.25 and 0.50 µm. The aerosol optical depths were calculated using Equation (7.3). When the predicted aerosol optical depth at 0.615 µm, calculated from the other measurements using the coefficients determined from the covariance analysis, was compared with the true values, the difference was found to be less than 1 %. The choice of wavelengths to be included in the covariance matrix to calculate the coefficients for this specific radiometer was a direct one because the wavelengths were well spaced. However, in situations where a large number of measurements are available, one should avoid including two very close wavelengths in the covariance analysis. We have found that doing so could lead to inaccurate ozone retrieval, and the best outcome is achieved by using only one of the close channels. Including information of the two close channels tend to bias the coefficients, since the same information is in twice, compared to only once for the other channels. 7 .2.2.2 King and Byrne method King and Byrne (1976; King et al., 1980) developed a method to predict the total ozone column, based on the assumption that a plot of natural logarithm of aerosol optical thickness versus natural logarithm of wavelength may be fitted to a quadratic of the form: ln(Tae,) = a +b ln(A)+c[ln(J)]2. (7.9) That is, if we start with some assumed total ozone correction, and perform a least squares fit, by altering the total ozone value until the best fit is achieved, we can determine the total ozone value. This technique was used by Michalsky et al. (1995), to derive the total ozone column from MFRSR measurements. 7.3 Results and discussion In this work the coefficients given in Table 7 .2 for average continental aerosol were used to predict the aerosol component of the 0.615 µm channel. Predictions using the other sets of coefficients in Table 7.2 gave very similar results. An eigenvalue Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 196 analysis was performed on the covariance matrix for the MFRSR set of kernels, including the 0.615 µm kernel. Since three of the measurement channels are contaminated by ozone, two approaches were adopted to determine the total ozone, an iterative method and a matrix method. For the iterative method, initially, only the 0.615 µm channel is assumed to be contaminated. A first estimate of the total ozone was calculated, and then this was used to correct the other two channels, 0.502 and 0.673 µm. Using the corrected values for 0.502 and 0.673µm, a new estimate for the aerosol contribution to 0.615 µm can be obtained and hence a new estimate for the ozone column. This procedure can be repeated iteratively until convergence is achieved. Alternatively, the solution can be found directly using a matrix method. By predicting the aerosol optical thickness component of the 0.615 µm channel, we end up with the same number of unknowns as there are equations, and thus there will be a unique solution. The problem can be put into matrix form, f(J)=Cp, (7.10) or in full matrix form, f(414) a414 1 0 0 0 Uoi f(502) aso2 0 1 0 0 Taer ( 414) f(615) = a61s b414 b502 b673 b870 X Taer (502) f(673) a613 0 0 1 0 Taer ( 673) f(870) as10 0 0 0 1 Taer (870) where f(.,1,) is aerosol optical thickness uncorrected for the ozone contribution, a-1 and U03 are as for equation (7.2), and b-1 is defined in equation (7.7). a-1 values are given in Table 7.3. The solution of the above problem will be (7.11) Both the iterative and matrix form solutions were used, and the results were identical. 7 .3.1 Comparison with TOMS The method outlined above for inferring total ozone, and that of King and Byrne, have been applied to data collected at University of New South Wales, School of Physics, Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 197 Table 7.3: Ozone absorption coefficient, a,i I I (atm cm), used for the MFRSR channels (Gregg and Carder, 1990). A(µm) 0 3 absorption coefficient, (a;) 1 / (atm cm) 0.414 0 0.502 0.032 0.615 0.110 0.673 0.044 0.870 0 Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 198 Sydney, between Sept. 25, 1996 and Sept. 16, 1997. Measurements for almost 170 days, with a clear period in either or both the morning and afternoon, were used, and the total ozone values obtained were compared with the values measured by the TOMS instruments on both the ADEOS and Earth Probe satellites for the same day, to evaluate the accuracy of the two estimates. Measurements of ADEOS / TOMS were only available until June 29, 1997. Figure 7 .1 shows the daily estimates of total ozone using both the present and King and Byrne method, alongside both the EP I TOMS and ADEOS / TOMS overpass measurements, plotted as a function of time. Figure 7.2 is the same as Figure 7 .1, but for the weekly mean average. The lines are the best fit of the mean values. Both methods show similar seasonal behaviour to the TOMS measurements. It is obvious that the new method gave a better agreement with the satellite measurements than King's method, which under-estimated the satellite values. The percentage difference between the weekly mean of the total ozone in both sets of estimates and corresponding ADEOS I TOMS and EP / TOMS measurements were calculated and is plotted as a function of time, in Figure 7.3. The present method agrees to within 1 % and 2 % respectively, while the ozone calculated using King's method underestimated the satellites values by 10 % and 11 %. The accuracy of the inferred total ozone using the new technique, compared to TOMS measurements, is at similar levels to those of ground-based total ozone measuring instruments. Results of such a comparison were reported by (Krueger et al., 1998) and (McPeters et al., 1998), who compared ADEOS / TOMS and EP/TOMS measurements with ground-based measurements made by a network composed of 45 mid-northern latitude stations with Dobson and Brewer ozone measuring instruments for the first study, and 30 stations for the second one. The weekly mean percentage difference was reported to be 1.5 % and 1 % respectively. Figure 7.4 and Figure 7.5 are the scatter plot of the weekly estimate of ozone, using the present method, with the averaged TOMS ADEOS and EP measurements for the same period, respectively. The correlation between the two values was excellent. The slope of the regression line of the estimated ozone column vs. ADEOS/TOMS and EP / TOMS respectively, is 0.97 ± 0.09 and 0.89 ± 0.089, with y intercept value of 5.8 ± 26 and 25.8 ± 26. R2 is 0.78 and 0.7. Figure 7.6 and Figure 7.7 are the same as above, except for the ozone values derived using King and Byrne X X t; t; • • '1,'o '1,'o J6< J6< • • ~- o o . ~- ,,;. ,,;. X method, method, " o o Jii< X X - Aug Aug o o X "x "x 0 0 ...,_ ...,_ Byrne Byrne ~ 0 0 ';. ';. X X 0 0 ~ ~ _1fo X X and and D D X X • • t. t. ,9 ,9 ~ ~ 0 0 -.':,,t -.':,,t Jui Jui o 0 ~ ~ King King o o 0 0 ~ >}1_ >}1_ , , . "' "' ,e ,e 'l< 0 0 + + .. .. ~ ~ '1< '1< 6 6 X X '\I'"";/,< '\I'"";/,< ;;): ;;): . . ~ ~ : "°Ii;; + + + + >C! analysis) Jun Jun 0 0 * * o • • tf9 tf9 ~) + + 1997 ;f ;f X X ";;< .. .. , , lt lt + . o o , , X,. ~ ~ -1: -1: 16 + .. .. + x X X A A 0 0 t t « eigenvalue eigenvalue X X • " ( ( May May .,-• x x + • X X .. .. September September method method 0 0 " " .,. .,. to to 0 0 6 6 ..,.. ..,.. X X Apr Apr x x • O O ';l ';l 1996 1996 O O (I) (I) + 0 0 ~ ~ present present J:<>< J. X X & & o o 25, 25, • • / / the the 0 0 :0, :0, + ~ ~ :,_ X X Mar Mar O O both both 0 0 'N' ,r 0 0 0 0 6 • • _. _. :-oO :-oO 0 0 • • + + Month Month September September using using +• +• 00 00 6 6 O O 11\, 0 0 a Feb Feb 0 0 from from + + 0 0 , , 4 4 X X 0 0 0 0 • • estimated estimated 0 0 0 0 6 ' ' + + + + + + + ~~ X X .. .. ~~ ~~ .Jt Jan Jan JI 0 0 _, _, :, :, Sydney, Sydney, : : + + a measurements 0 0 + + 0 0 at at 0 0 0 0 + + + + O.o O.o cj' cj' ~ ~ "l.t.: "l.t.: XX XX ~ Dec Dec ozone ozone + o o ~ xx ~ EPffOMS EPffOMS + + o + + total total ! + + and and of of X X + + ~ X X Nov Nov ~ ~ + + xx x+ x+ 00 00 • • o o ADEOS ADEOS X X "'+ Byrne Byrne O O 4 4 Method Method 0 0 • : : measurements measurements X X Oct Oct X X 0 ~ ~ + + and and + + j.O< j.O< X X X X i i 4< 4< ,: ,: >Ji't >Ji't ~ 0 EP EP Present Present King King ADEOS ADEOS MFRSR MFRSR 0 0 + x o o 4 4 Sep Sep ~ ~ 7t 7t Daily Daily : : .1 .1 7 7 Figure Figure 170 170 190 190 210 210 230 230 250 250 270 270 290 290 310 310 330 330 350 350 370 370 390 390 410 410 0 0 ~ ~ ~ ~ e. e. S- Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 200 400 350 300 -::) 250 0 -w z 200 0 --King and Byrne Method N 150 0 --Present Method 100 --ADEOS 50 ---EP 0 Aug Oct Dec Feb Apr Jun Aug Oct Month Figure 7.2: Time series of the ozone column smoothed weekly mean averages of the four sets of measurements. 15.% en ~ 10.% 0 I- 5.% "C ~ 0 0.% .c -Q) -5.% -r- ~ Q) -10.% V 0 "' C ,__Q) -15.% A & -20.% 0 - Present Method-EP C -25.% - King And Byme-EP -Q) ~ - • Present Method-ADEOS Q) -30.% a.. • • • King And Byme-ADEOS -35.% ,__ ,__ >, a. 0 > 0 C .0 C :5 O'> a. Q) - 0 Q) co Q) co a. co :, -, :, Q) u <( -, en 0 z 0 -, u.. ~ ~ <( en 0 Month Figure 7.3: Percent difference between the weekly mean estimates of the ozone column using both the Present and King's techniques and corresponding ADEOS / TOMS, EP / TOMS total ozone measurements. Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 201 -o 360 +--,--.,...... ,,...... ,.--+---r---,--.-.....-+--,...... ,.,...... ,.~-+---,-~--.,...... ,.f- 0 .c A"esent Method vs.ADEOS -Q) y = (0.97±0.09)x + (5.8±26.2) •• X R2=0.78 X ~ 320 X •• ··> Figure 7.4: Scatter plot of the weekly mean average of the estimated total ozone using the present method (eigenvalue analysis), versus TOMS/ ADEOS ozone measurements. The dotted line is the line of best fit. -o 360 .c0 A"esent Method vs EP -Q) y = (0.89±0.089)x + (25.77±26.0) .e-· R2 = 0.7 ~- ~ 320 0 0 •• ff C: -Q) •• ·O II') ~ c.. 280 S' 0 ~240 0 z 0 N 0 200 200 240 280 320 360 OZONE (DU), EP Figure 7.5: Scatter plot of the weekly mean average of the total ozone estimated using the present method (eigenvalue analysis), versus TOMS/ EP ozone measurements. The dotted line is the line of best fit. Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 202 360 +-,-,--,-.,-+-,-....-,-,--+-,--,-.-,--,-+-,--.-,,_.,.._._ (1) C '- KING and Byrne Method vs ADEOS >, a::i y =( 0.88±0.12)x + (10.83±33.27) -c 320 R2 = 0.64 x~.··· C (tJ X C) C ~ 280 X -:::, --0 zw 240 0 N XX 0 200 200 240 280 320 360 OZONE (DU), ADEOS Figure 7 .6: Scatter plot of the weekly mean average of the estimated total ozone using King and Byrne method, versus TOMS/ ADEOS ozone measurements. The dotted line is the line of best fit. 360 (1) C KING and Byrne Method vs EP '- >, y = (0.84±0.11)x + (18.63±32) a::i R2 = 0.57 -c 320 C .. (tJ 0 .. C) 13• C 0 •• • ~ 280 o.e···· o o o q.• ro o -:::, o o_.o:> 0 oO O.• _o- Figure 7.7: Scatter plot of the weekly mean average of the estimated total ozone using King and Byrne method, versus TOMS / EP ozone measurements. The dotted line is the line of best fit. Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 203 method. The slope is 0.88 ± 0.12 and 0.84 ± 0.11, they-intercept is 10.8 ± 33 and 18.6 ± 32 and R2 is 0.64 and 0.57. It is can be seen clearly that the correlation with the new method gave less scatter, better agreement with the satellite measurements, with a slope closer to 1.0 and larger R2• Measurements of both TOMS ADEOS and EP were almost identical, as it can be seen in Figure 7 .8. The work presented here for inferring total ozone column from spectroradiometer measurements will improve the accuracy of aerosol optical thickness determination, as well as improving the aerosol size distribution, estimated from optical thickness measurements. It will also provide a cheap, reliable, and easy to use alternative for measuring the total ozone column, without the need of additional measuring channels. Eigenvalue analysis could also provide a more accurate prediction of aerosol at other channel, such in 0.94 µm region, to estimate the water vapour column abundance. Details of such predictions are presented in Chapter 8. 7.4 Summary and conclusion A new technique for inferring total ozone column and aerosol optical thickness from spectral measurements, using eigenvalue analysis, has been presented here. It has been applied to data collected by an MFRSR over a period of 12 months, and the results compared with those obtained by applying the King and Byrne (1976) method to the same data set. Both sets of estimates were compared with corresponding TOMS measurements to evaluate their accuracy. The estimated total ozone using the new method showed an excellent agreement with the satellite measurements. The weekly mean percentage difference between the inferred total ozone using the new method and TOMS was calculated and the values obtained (1 % and 2 % for ADEOS and EP respectively) were comparable with the values reported for a comparison between TOMS and a network of ground-based Dobson and Brewer instruments (1.5 % and 1 %). The method presented here allows the total ozone column at 0.615 µm to be determined with a high degree of accuracy. As a by-product, it also allows the correction of ozone-affected aerosol channels, which should lead to more accurate inversions for size distribution. Such accuracy is achieved without the need for additional measurements. Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 204 360 en ADEOS vs. EP 0 y =(1.012±0.022)x - (3.69±6.51) w 320 R2 = 0.98 0 <( -::::, 0 280 -w z 0 N 240 0 200 200 240 280 320 360 OZONE (DU), EP Figure 7.8: Scatter plot of the weekly mean average of total ozone column, measured using TOMS I ADEOS versus TOMS/ EP. The doted line is the line ofbest fit. Chapter 7 Inferring Total Ozone and Aerosol Optical Thickness ... 205 Eigenanalysis can be used to predict aerosol optical thickness of other channels, for example that at 0.934 µm, to estimate the water vapour column. The same calculation will be repeated, after the addition of the water vapour channel to the covariance matrix, to produce a new set of coefficients required for such calculation. Chapter 8 Water Vapour Column Abundance Retrieval In the 0.936 µm Region 8.1 Introduction Water vapour plays a crucial role in the atmospheric process, from global climate to micrometeorology. It is the most variable major constituent of the atmosphere and the largest contributor to the greenhouse effect. It affects the global climate system, directly, by absorbing and radiating energy from the sun, and indirectly, by its effect on cloud formation, aerosol growth, and the chemistry of the lower atmosphere. It plays a critical role in many of the chemical reactions that occur in the atmosphere. Therefore, an accurate estimation of the vertical water vapour column is of a great importance to communication and remote sensing applications. For many years, radiosonde instruments were the main means of measuring the water vapour column throughout the atmosphere, however, they are labour intensive and because of their high costs, only launched twice a day. Sun photometers or radiometers, which are usually used for aerosol particle measurements, offer an alternative to radiosondes in retrieving column abundances of water vapour, when an appropriate filter is used, such as the 0.940 µm band. They are easily operated and provide continuous measurements during the daytime, with a minimum cost. The disadvantages of these instruments are that they can provide data only under clear sky conditions, and they do not recover vertical profile information. In recent times, radiometers have been increasingly used to measure atmospheric transmission in the visible and near IR. Several studies have been done estimating the column water vapour amount using the absorption in the A= 0 .940 µm region (e.g., Bruegge et al., 1992; Thome et al., 1992, and 1993; Frouin et al., 1990; Michalsky et al., 1995; Shiobara et al., 1996). In this chapter, the water vapour column abundance is retrieved from the MFRSR solar transmittance measurements in the 0.934 µm band, using the Modified Langley technique of Bruegge et al. (1992). We also investigate the use of the Chapter8 Water Vapour Column Abundance Retrieval... 207 atmospheric models LOWTRAN 7 and MODTRAN 3 in determining the instrument coefficients, required for water vapour retrieval. A new method for retrieving aerosol optical thickness at 'A= 0.934 µm from other measurements, using eigenvalue analysis is proposed, and compared with aerosol optical thickness predicted using the quadratic form proposed by King and Byrne (1976). Results of the inferred temporal and seasonal variations of the water vapour column abundance in Sydney are presented and analysed. 8.2 Methodology 8.2.1 Modified Langley Plot In order to compute the water vapour transmittance, it is necessary to remove the influence of the molecular and aerosol scattering. In narrow band filters, which are usually used in radiometers to measure the direct solar irradiance under clear sky conditions, spectral attenuation of light is described as (8.1) or in( V,.) = in(~" R-2 ) - r,. m (8.2) where Vi. is the instrument output voltage, Vm .. is the instrument calibration constant, and R is the Earth-Sun distance. However, in regions of strong spectral variation of molecular absorption, the relationship must be modified to include the water vapour effects: (8.3) where T w(A) is the water vapour transmittance. Since the gaseous absorption is not linear in air mass, it cannot be resolved using the standard Langley method. Bruegge et al. (1992) used a modified Langley approach. For the measurements of the 0.934 µm channel, the last term of Equation (8.2) is replaced by where TR and t'aer are the Rayleigh and aerosol optical thickness components Chapter 8 Water Vapour Column Abundance Retrieval... 208 respectively, u is the column water vapour, and k and b are constants of a particular filter. The above equation is displayed in a logarithmic form as (8.4) A plot of In( V) + ( r R + 1' aer )m against mb for clear and stable days, with fairly homogenous water vapour distribution, will yield a straight line whose slope is kub and a y-intercept of ln(VoK2). Usually intervals in morning and afternoon, when m lies between 2 and 6, are used. In order to solve this equation for u, we need to determine k and b from modelling, and 'taer from measurements. 8.2.1.1 Prediction of aerosol optical thickness In order to apply the modified Langley technique, aerosol optical thickness, t'aer, needs to be known. t'aer at A= 0.934 µm will be predicted from other optical thickness measurements, after corrections for Rayleigh scattering and ozone absorption are made, using both the eigenvalue analysis technique, and quadratic form. Eigenvalue analysis Ozone absorption is corrected using eigenvalue analysis (Chapter 7). Then the aerosol measurement at 0.934 µm, gm, can be calculated from other measurements, gi, using (8.5) The coefficients, b1, were calculated for 3 different types of aerosol, using the eigenvector associated with the smallest eigenvalue. Values of these coefficients are given in Table 8.1. (For more details of the eigenvalue analysis, see Chapter 7). Quadratic form King and Byrne (1976), proposed a method to predict the total ozone column, assuming a quadratic dependence between the natural logarithm of the aerosol optical thickness, versus the natural logarithm of wavelength (Chapter 7 Section 7.2.2.2). After correcting for ozone absorption using King and Byrne method, t'aer is then extrapolated to 0.934 µm using the same quadrature form in equation (7.9) (Michalsky et al., 1995). Table 8.1: Coefficients used for prediction of 0.934 µm measurements for MFRSR wavelengths. Wavelength, 'A(µm) Aerosol Type 0.414 0.502 0.616 0.673 0.870 Average Continental -0.001 -0.018 0.415 -0.709 1.301 Urban -0.047 0.093 0.423 -0.865 1.391 Dust-like 0.009 -0.056 0.459 -0.709 1.282 Chapter 8 Water Vapour Column Abundance Retrieval... 210 8.2.2 Water vapour transmittance model To convert measured water vapour transmittance, Tw(AJ, into u, theoretical models are used to determine Tw (A) centred at a particular filter wavelength, A, along a slant path ofmby (8.6) Tw(A) is the transmittance due to the gas absorption, calculated with respect to the equivalent total absorber amount, which is accumulated along the slant path m,f(A) is the filter function or spectral response. The relative response for the MFRSR water vapour filter was measured by the manufacturer at 24 different wavelength points using a monochromator. To perform more accurate integration of the above equation, calculations had to be made to extrapolate the response for 275 more wavelength points. Figure 8.1 is a plot of the actual measurements and the calculated spectral response. Tw (A) is usually calculated using atmospheric transmittance models. In this work we will use LOWTRAN 7 (Kniezys et al., 1988) and MODTRAN 3 (Berk et al., 1989; Anderson et al., 1993). The main difference between the two models is spectral resolution, 20 cm- 1 and 2 cm- 1 respectively. Figure 8.2 is a plot of the transmittance modelled using LOWTRAN 7 and MODTRAN 3 along a vertical path through the midlatitude summer atmosphere, of 2.92 cm vertical column of water vapour, and the spectral response for the MFRSR channel. Parameterisation of water vapour transmittance LOWTRAN 7 and MODTRAN 3 calculations are performed in the transmittance mode with no aerosol loading and midlatitude summer conditions. Water vapour transmittance is calculated from sea level to the top of atmosphere, in the spectral region '"A = 0.920 to 0.960 µm, for solar zenith angles, 0s =0° to 85°, which corresponds to m = 1 to 11. Other studies showed only a small difference between midlatitude summer, winter and tropical (Michalsky et al., 1995; Halthore, et al., 1997). Chapter 8 Water Vapour Column Abundance Retrieval... 211 --calculated 1 x measured Q) g 0.8 ~ ·E o.s II) C: ~ 0.4 0.2 O-"-'"...... _~..._...... ,_...._...... ,...... """'"+ ...... ,...-.i..4- 0.915 0.92 0.925 0.93 0.935 0.94 0.945 Wavelength, A(µm) Figure 8.1: Real MFRSR spectral response as measured by a monochromator, and recalculated spectral response as a function of wavelength 11.(µm). . . summer summer midlatitude 3 for for 0.965 0.965 MODTRAN MODTRAN transmittance transmittance and and 7 7 apour apour 3 3 7 7 0.955 0.955 v response response water water LOWTRAN LOWTRAN by by MODTRN MODTRN LOWTRN LOWTRN Filter Filter atmospheric atmospheric 0.945 0.945 A.(µm) A.(µm) ---- computed computed on on as as \ \ water, water, superimposed superimposed Wavelength, Wavelength, 0.935 0.935 precipitable precipitable of of cm cm I I transmittance transmittance filter filter of2.92 of2.92 0.925 0.925 content content _/ _/ narrow-band narrow-band water water 915 915 ofa ofa . 0 0 0 1 1 6 6 MFRSR MFRSR . 0.2 0.2 0.4 0.4 0 0.8 0.8 The The : : f6 f6 ~ ~ Cl) Cl) '- ffi ffi 2 :i:::: :i:::: I- .E .E . 8 Figure Figure Chapter 8 Water Vapour Column Abundance Retrieval... 213 Therefore there was no need to repeat this calculation for other conditions. The next step is to calculate the wavelength integrated transmittance Tw(A), using Equation (8.6). Results of calculation can be expressed in a two-parameter model as (8.7) Figure 8.3 and Figure 8.5 are the water vapour integrated transmittance, Tw, plotted versus slant path water vapour amount, in cm, determined using LOWTRAN 7 and MODTRAN 3 respectively. In order to obtain coefficients k and b, Equation (8. 7) needed to be rearranged into logarithmic form as In( In( 1 / Tw)) = In( k) + b In( um) (8.8) A plot of ln(ln(Tw)) versus In(um) should give a straight line whose slope is b, and y intercept is ln(k). Figure 8.4 and Figure 8.6 are plots of ln(ln(Tw)) versus In(um), determined by LOWTRAN 7 and MODTRAN 3 respectively. Both plots show a regression fit R2 very close to one. Table 8.2 is a summary of the coefficients k and b obtained. Tw was recalculated using the obtained coefficients k and b. It is obvious that there is some difference between the values of the coefficients obtained by LOWTRAN 7 and MODTRAN 3, which is caused by the different spectral resolution of the models, thus we believe that the values obtained by MODTRAN 3 are more accurate, and we chose to use only these coefficients. In a previous work conducted by Schmid et al. (1996), they compared modelled and empirical approaches for retrieving column water vapour from solar transmittance measurements in the 0.940 µm region. They found that, with respect to experimental data, LOWTRAN 7 and MODTRAN 3 resulted in an overestimation in Tw retrieval of 18 - 30 % and 7 - 20 % respectively. Michalsky et al. (1995) used MODTRAN 2 for their water vapour column retrieval. The comparison between the Microwave radiometer and the MFRSR resulted in a 12 % difference. Chapter 8 Water Vapour Column Abundance Retrieval... 214 0.4 Q) 0 Tw (.) 0.3 C -calc. Tw ea ~ E 0.2 Cl) C ....ea I- 0.1 0 0 10 20 30 Slant path Water vapor, mu (cm) Figure 8.3: Water vapour integrated transmittance Tw, determined using LOWTRAN 7, versus slant path water vapour amount. The calculated Tw was determined using k = 0.5739 , b = 0.4862. 1.5 1.1 :::::: y = 0.4862x - 0.5553 ~ 1-- R2 = 0.998 ..... 0.7 -C ;::::... C 0.3 -0.1 0 1 2 3 4 ln(um) Figure 8.4: A plot ofln(ln(l/Tw), determined using LOWTRAN 7, versus ln(um), the straight line is determined using least square fitting procedure to these point. The slope of the line is b, while k is the log of the y-intercept. Chapter 8 Water Vapour Column Abundance Retrieval... 215 0.4 o Tw Q) -calc(tw) (.) 0.3 C ea ~ .E 0.2 en C .._ea I- 0.1 0 0 0 0 10 20 30 Slant path Water vapor, mu (cm) Figure 8.5: Water vapour integrated transmittance Tw, determined using MODTRAN 3, versus slant path water vapour amount. The calculated Tw was determined using k = 0.6053, b = 0.5184. 1 0.8 y = 0.5184x - 0.502 -:t 0.6 R2 = 0.9993 ...... T"" -C ::::.. 0.4 C 0.2 0 0 1 2 3 ln(um) Figure 8.6: A plot ofln(ln(l/Tw), determined using MODTRAN 3 versus ln(um), the straight line is determined using least square fitting procedure to these point. The slope of the line is b, while k is the log of the y-intercept. Chapter 8 Water Vapour Column Abundance Retrieval... 216 Table 8.2: Coefficients k and b obtained for the MFRSR using LOWTRAN 7 and MODTRAN 3, midlatitude summer, R2 is regression coefficient for the line of best fit. Model k b LOWTRAN7 0.5739 0.4862 0.998 MODTRAN3 0.6053 0.5184 0.9993 Chapter8 Water Vapour Column Abundance Retrieval... 217 8.3 Results Both the Eigenvalue analysis and quadratic form relationship were used to predict the aerosol optical thickness, t'aer at A= 0.934 µm, using the measurements of a MFRSR for almost 155 days, with a clear period in either or both the morning and afternoon. Coefficients given in Table 8.1 for average continental aerosol were used to predict the aerosol component of the 0.934 µm channel. Results of the two methods of estimating the aerosol optical depth show a very small difference between the two predicted values. Figure 8.7 and Figure 8.8 are plots of the predicted t'aer using eigenvalues analysis versus Tuer using quadratic form, for the morning and afternoon respectively. Both figures show a slope= 1, and ay-intercept = 0.0, with a regression coefficient of R2 = 0.99. Both methods were able to predict t'aer with similar values, therefore either of the methods can be used, and should produce identical results. Because of the minimal effect of the aerosol optical thickness in the 0.934 µm channel, compared to the water vapour contribution, differences between the two predicted values of t'aer will not be significant. 8.3.1 Column water vapour retrieval Continuous water vapour column can be estimated by combining Equations (8.3) and (8.7) (8.9) In order to solve this equation, it is required to know the value of the MFRSR channel calibration constant V0 • This was found using the modified Langley Technique as described above. Figure 8.9 is Langley plot and the least squares best fit line for the morning of April 29 1997, which was a fairly clear and stable day. However, determining V0 on any given day was not always possible, since the atmosphere was not stable for the majority of the measurements. Instead, an average value of Vo had to be determined for the entire set of measurements. Figure 8.10 is a time series plot of the estimated V0 , corrected for Earth-Sun distance R, for the whole 155 sets of data. Chapter 8 Water Vapour Column Abundance Retrieval... 218 0.16 u, -·u; >, y = 1.00x + 0.00 ea 0.12 2 C R = 0.99 ea (l) ::::J ai 0.08 > C (l) C) uJ 0.04 ... -G) ~ 0 0 0.04 0.08 0.12 0.16 't'aer (Quadratic form) Figure 8. 7: Predicted aerosol optical thickness, 'taer, at A.= 0.934 µm for the morning period, using Eigenvalue analysis versus 'taer predicted by quadratic form. 0.12 .!Q y = 1.01x + 0.00 -u, 0.1 >, 2 ea R = 0.99 C ea 0.08 (l) ::::J ea 0.06 > C (l) C) 0.04 uJ -lil 0.02 ~ 0 0 0.02 0.04 0.06 0.08 0.1 0.12 't'aer (Quadratic form) Figure 8.8: Predicted aerosol optical thickness, 'taer, at A= 0.934µm, for the afternoon period, using Eigenvalue analysis versus 'taer predicted by quadratic form. Chapter 8 Water Vapour Column Abundance Retrieval... 219 0 E -0.4 ">, er +~ -0.8 Q) -+ -1.2 • ~ -.s -1.6 -2 1.4 1.6 1.8 2 2.2 2.4 Figure 8.9: Modified Langley plot for the morning of April 29 1997. 'taer = 0.0422, '!Ray= 0.012. Solid line is the least square best fit line. 1 0.8 1 0.6 ~ffi V > ~~~-Jf¥1~~Jllrv 0.4 0.2 0 +-' () C .0 L.. 1-1..>-c 0) 0) () > ~ a. 0 Q) ea Q) ea a. a. ea ~ -, ~ ~ Q) 0 z Cl -, LL ~ <( <( ~ -, 2 Figure 8.10: A plot of the MFRSR calibration constant, V0 in W/m /nm as obtained at').,,= 0.934 µm, from October 1996 to September 1997. Chapter 8 Water Vapour Column Abundance Retrieval... 220 There is no apparent trend with time in the estimated V0 • The computed average value 2 of the calibration constant was V0 = 0.689 W/m /nm with a standard deviation of a= 0.083. Figures (8.11, 12, 13, and 14) shows temporal variation of the water vapour column, u (gm cm-2), as observed by the MFRSR 0.934 µm channel, for June 10, August 1, February 19, and April 29 1997 respectively. Variability of water vapour column with time was relatively small during June 10 and August 1, with fairly low value of u = 0.5 gm cm-2• On the other hand, it was reasonably unstable during February 19 and April 29, with a higher average water vapour column of u = I.I and 0.9 gm cm-2 respectively. No independent measurements of water vapour column were available on site, therefore, a validation of the measurements was not possible. However, previous published works have found a reasonable agreement between radiometer and other instruments: 10 % agreement with radiosonde measurements (Bruegge et al., 1992), 12 % with microwave radiometer measurements (Michalsky et al., 1995). Figure 8.15 is a plot of the averaged water vapour column retrieval, u (gm cm-2), derived using the Modified Langley technique, for the morning and afternoon periods, measured from September 25 1996 to September 16 1997. It shows an apparent seasonal variation, where a maximum observed values of u were measured during the summer season (Dec., Jan., and Feb), while the smallest values of u were measured during the winter season (Jun., Jul., and Aug.). Similar seasonal pattern was observed by Halthore et al. (1997), who presented results of the amount of precipitable water derived from a narrowband Cimel sun photometer network around the world. Water vapour retrieval showed a similar seasonal variation of high values during Summer season, and low values during Winter season. 8.4 Summary and conclusion The Multifilter Rotating Shadowband Radiometer MFRSR solar transmittance measurements in the 0.934 µm band were used to retrieve the water vapour column abundance, using the Modified Langley technique. Atmospheric models LOWTRAN 7 and MODTRAN 3 were used to calculate the water vapour transmittance, centred at 'A= 0.934µm, for different solar zenith angles, in order to obtain the instrument coefficient k and b. Chapter8 Water Vapour Column Abundance Retrieval... 221 N-1 'E 0 E0.8 O> -:::i .0.6 C E ::, g0.4 ,._ c.0 ~0.2 ,._ Q) ~ 0------'---+-----'--' 21 22 23 0 1 2 Time (UTC) Figure 8.11: Water vapour column retrieval, u (gm cm·2J, on June 10 1997. Local time is UTC + !Oh. _1 N 'E 0 E0.8 - O> -:::i {0.6- ~~~~ g0.4 - ,._ c.0 ~0.2 - ,._ Q) ~ 0------'------..._-,-----'--~ 21 23 1 3 5 Time (UTC) Figure 8.12: Water vapour column retrieval, u (gm cm-2), on August 1 1997. Local time is UTC + !Oh. Chapter 8 Water Vapour Column Abundance Retrieval... 222 _ 1.6 ------, ~(.) 5, 1.2 -:::, E0.8C: ::I 0 (.).... g_o.4 co ....> Q) ~ro o------20 22 0 2 4 6 Time (UTC) Figure 8.13: Water vapour column retrieval, u(gm cm·2), on February 19 1997. Local time is UTC + I Oh. N-1 'E (.) E0.8 C) -:::, -0.6 C: E ::I g0.4 .... 0 a. ~0.2 .... Q) ~ o------'------21 23 1 3 Time (UTC) Figure 8.14: Water vapour column retrieval, u (gm cm·2), on April 29 1997. Local time is UTC + 1Oh. 2 -+---...,.---+-...,.---+-...,.---+-...,.---+-...,.---t---...,.---t---...,.---t---...,.---t---...,.---t---...,.---t---...,.---t---...,.--"1r 1.6 X X JC 00 X X ~ 0 Xx E (.) 05 " 8 E C) " " " " 0 O Geo'l, O z~ Oo •Jt Jt Q) X 0 (.) O ~ OQ!J(z O '!f oo,o It o o &J dJ C 0 o' 0 ~ 0 "'O .,P "" :f eo '!fiE " C X acii 8 ooo1' -:~'3.\ 8 ,j. :::, 0 JC "'* o o , o .0 "3>0 ,16 0 'I) < 0 • " " '*" °"~ C o lf o 'IJ: I ) E :::, o u(pm) - ~ o 0 X X X (.) JCu(am) 0 -t---'--+--..__+--,__1----'--t---'---+~--t-~--+-~-+-~-~-~-+-~-+- Oct Nov Dec Jan Feb Mar Apr May Jun Jui Aug Month Figure 8.15: Temporal variation of the averaged water vapour column, u (gm cm"2), as observed by the MFRSR From September 25 1996, to September 16 1997, for the morning and afternoon periods. Chapter8 Water Vapour Column Abundance Retrieval... 224 An alternative method to predict the aerosol optical thickness at A= 0.934 µm, using Eigenvalue analysis was introduced and compared with the aerosol optical thickness predicted using quadratic form relationship, proposed by King and Byrne (1976). Both methods produced similar results of aerosol optical thickness. The difference between the two predicted values was very small, and insignificant compared to the effect of water vapour transmittance. However, the technique will provide a fast, reliable, and easy to use alternative for such predictions. Temporal and seasonal variations of the water vapour column abundance were investigated. An apparent seasonal pattern of maximum water vapour column values during summer season and minimum values during winter season was observed during a whole year of measurements. For the future, the availability of other measurements of water vapour column, such as radiosonde or microwave radiometer, would provide an independent and reliable means of validation of the MFRSR measurements. Chapter 9 Conclusion The main objectives of this thesis were to use ground based remote sensmg measurements of the MFRSR and available in situ measurements of chemical composition (IBA analysis), scattering coefficient (nephelometer), absorption coefficient (LIPM), mass concentration (TEOM) and relative humidity, to extract maximum amount of information about Sydney aerosol and its variations over time. These main objectives were achieved, given the limited available resources, and the time constraints of a Ph.D. research work. The MFRSR was installed in Sydney in late 1995, with almost two years of measurements collected and analysed for this study. Seasonal variations of aerosol optical thickness, scattering and absorption coefficients, particle hygroscopic growth, chemical composition, and size distribution were studied, and analysed, as well as the radiatively active gases ozone and water vapour. Such measurements, over a long period of time, were not available before for the Sydney area, and will improve our knowledge of aerosol particles, ozone and water vapour column properties, and help building more accurate radiative forcing models, as well as improving means of measuring particles properties. The MFRSR was compared to other measurements when possible. Comparison of aerosol scattering optical thickness and total column mass with scattering coefficient and mass concentration were made. For this purpose, column and surface measurements needed to be related by an effective scaling height. Since the in situ measurements are dry measurements, it was necessary to correct these measurements for the impact of relative humidity. This was achieved by calculating an enhancement growth factor, using chemical data available from ANSTO, and the chemical thermodynamic model SCAPE2. The calculated effective scale height values, of 2 and 2.6 km from scattering measurements and 1.6 and 1.74 km from mass measurements, for both the morning and afternoon, indicate that the majority of aerosol particles are concentrated at or near the surface. However, it illustrated the difficulty of predicting the vertical aerosol optical Chapter 9 Conclusion 226 thickness or total mass concentration from in situ measurements, such as nephelometer or TEOM, without prior knowledge of the vertical profile of the boundary layer, as aerosol particles do exist in several altitude layers. The intensive study to test and validate the absorption coefficient measurements, using the ANSTO's integrating plate method (LIPM), will improve the accuracy of such measurements significantly. It was found that the value of mass absorption coefficient £ = 10 m2g- 1 previously used for mass determination, and widely accepted for diesel fuel emission, is an inappropriate choice. Accurate estimates of£= 7 and 6 m2f 1 for soot and ambient aerosol particles were experimentally determined. The experimentally determined mass absorption coefficient was in good agreement with the theoretically determined value. IBA analyses were used to predict the refractive index and density of the Sydney black carbon. The predicted value for the refractive index was m = (1.71 ± 0.07)-(0.38 ± 0.13)i, and p = (0.83 ± 0.3) g/cm3 for the density. Empirical models of enhancement growth factor, refractive index and density of ambient aerosol particles, as a function of relative humidity, were determined using the aerosol chemical composition. Particle hygroscopic growth factor was calculated for each month, and for the whole year. Maximum growth of aerosol particle was noticed during the winter season, while minimum growth occurred during summer season. Obtained results yielded a refractive index of 1.73 to 1.51 for a humidity range of 0.3 to 0.9. The aerosol particle density (using molar fraction approach), varied from 1.9 g/cm3, to 1.44 g/cm3, for the same range of relative humidity. The resultant density using mass fraction approach was higher by -0.2 g/cm3. Complex refractive index (K) and single scattering albedo (m) of aerosol column were derived using the MFRSR diffuse to direct ratio. The average value of K = 0.0369 ± 0.0135, and m = 0.7362 ± 0.0281. These key aerosol parameters were previously unknown for Sydney aerosol, and people had to rely on values reported in the literature. This work demonstrated the use of the accelerator ion beam analysis (IBA) elemental composition measurements, and thermodynamic models, in predicting these key parameters. Work also included investigating the use of the constrained linear inversion technique and analytic eigenfunction theory to obtain the aerosol size distribution, using synthetic data. It was shown that constrained linear technique would be most suitable for Chapter 9 Conclusion 227 the type of aerosol observed in Sydney. A whole year of measurements was analysed and results of inversion showed a bimodal distribution as the dominant type of aerosol in Sydney, with mode radii of rm = (0.10 -0.35) µm and rm = (0.6 -0.85) µm. A new technique for inferring total ozone column and aerosol optical thickness from spectral measurements, using eigenvalue analysis, has been presented here. Results of inferred ozone column showed an excellent agreement, of 1% and 2%, with satellite ozone measurements obtained using ADEOS and EP / TOMS instruments. The new method showed accuracy levels similar to those of ground-based total ozone measuring instruments such as Dobson and Brewer, with a fraction of its costs, and without any additional measurements. Eigenvalue analysis was also applied to the problem of determining column water vapour, using radiometer measurements. The method showed similar values to those obtained using the quadratic form relationship. The prediction of aerosol optical thickness at 0.934 µm region was not so critical, since differences between the values determined using the two method were insignificant, compared to the effect of the water vapour transmittance. However, the technique provides a fast, reliable, and easy to use alternative for such measurements. Long-term measurements were used to study the diurnal and temporal variability of aerosol properties, as well as ozone and water vapour column abundances. The highest mean of the monthly average of aerosol optical thickness, for the morning and afternoon, was observed in January, and summer was the season that had the highest mean value. An apparent seasonal pattern of maximum water vapour column values during summer season and minimum values during winter season was observed during a whole year of measurements. 9.1 Future work and recommendations Work presented here demonstrated how various independent measurements of aerosol properties could agree with each other. This work could be improved significantly if additional aerosol characteristics or properties were available, such as in situ measurements of: continuous absorption coefficient, ambient scattering coefficient, size distribution and the size resolved chemical composition of aerosol particles. In situ absorption measurements were only available for a 24 hour period, twice a week, and Chapter 9 Conclusion 228 therefore, it could not be used, alongside scattering measurements, to predict a value of single scattering albedo. Continuous absorption coefficient will be more suited for such predictions. Ambient scattering coefficient measurements are essential to obtain an experimentally determined enhancement growth factor, needed to assess the accuracy of the calculated value. While a comparison between the MFRSR inverted aerosol distribution, and direct in situ size distribution measurements, would give valuable information about the aerosol vertical profile predictions. In order to accurately correct the particle size for hygroscopic growth, a size segregated chemical composition is needed. It is also of a great importance to conduct these measurements at different altitudes to obtain the vertical profile of aerosol particle loading. Such measurements would be vital to obtain more accurate and comprehensive tests of closure or consistency studies between remote sensing and in situ measurements as well as the models that link them. 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Binkowski, 2000, "A comparative review of inorganic aerosol thermodynamic equilibrium modules: similarities, differences, and their likely causes", Atmospheric Environment, 34, pp. 117-137. Appendix A Appendix A. I: Summary of chemical composition of candle carbon measured on Teflon filters Candle Carbon Avg. s.d. Avg. % s.d. % Mass (µg/cm2) 44.0 29.2 100.0 Soil (µg/cm2) 0.34 0.21 0.80 0.28 Sulfate (µg/cm2) 0.99 1.3 2.2 1.9 Koon Smoke (µg/cm2) 2.8 3.3 6.1 5.8 Organic Matter (µg/cm2) 7.5 3.3 18.1 5.2 Trace Elements (µg/cm2) 0.13 0.07 0.4 0.3 Est. Elt. C (µg/cm2) 32.8 22.9 70.0 10.3 Appendix A 243 Appendix A.2: Summary of chemical composition of acetylene carbon measured on Teflon filters Acetylene Carbon Avg. s.d. Avg. % s.d. % Mass (µg/cm2) 28.5 27.4 100 Soil (µg/cm2) 0.17 0.08 0.93 0.66 Sulfate (µg/cm2) 0.03 0.02 0.2 0.2 Knon Smoke (µg/cm2) 0.00 0.004 0.02 0.02 5 Organic Matter (µg/cm2) 8.2 4.0 36.8 19.1 Trace Elements (µg/cm2) 0.72 0.69 3.41 4.27 Est. Elt. C (µg/cm2) 22.5 24.0 58.0 22.5 Appendix A 244 Appendix A.3: Summary of chemical composition of graphite carbon measured on Teflon filters. Graphite Carbon Avg. s.d. Avg. % s.d.. % Mass (µg/cm2) 115.4 106.8 100.0 Soil {µg/cm2) 0.77 0.32 2.07 2.54 Sulfate {µg/cm2) 0.34 0.11 1.14 1.62 Knon Smoke {µg/cm2) 0.01 0.01 0.12 0.10 Organic Matter {µg/cm2) 8.1 7.0 11.0 11.6 Trace Elements {µg/cm2) 0.14 0.03 0.51 0.69 Est. Elt. C (µg/cm2) 106.1 99.6 85.2 16.0 Appendix A 245 Appendix A.4: Summary of chemical composition of candle carbon measured on Nuclepore filters Candle Carbon (Nuclepore) Avg. s.d. Avg. % s.d. % Mass (µg/cm2) 7.90 5.97 100.0 Soil (µg/cm2) 0.33 0.03 5.8 4.2 Sulfate (µg/cm2) 0.18 0.03 4.0 4.7 Koon Smoke (µg/cm2) 0.008 0.022 0.02 0.23 Organic Matter (µg/cm2) 1.56 1.05 18.1 0.0 Trace Elements (µg/cm2) 0.06 0.02 1.2 1.3 Est. Elt. C (µg/cm2) 6.46 4.69 72.0 7.0