Infrared extinction spectra of mineral dust aerosol. Mineral dust aerosol play a significant role in the atmosphere, however, a comprehensive

Transcription

1 Infrared extinction spectra of mineral dust aerosol. Introduction Mineral dust aerosol play a significant role in the atmosphere, however, a comprehensive understanding of its role in climate is lacking. Atmospheric dust can directly affect the atmosphere by absorbing and scattering of radiation. 1 Mineral dust aerosol may have a cooling effect by scattering incoming ultraviolet radiation and a heating effect by absorbing outgoing terrestrial infrared (IR) radiation. In order to determine the effect of atmospheric dust on climate various remote sensing techniques are used. For correct remote sensing data processing it is important to take the radiative effect of atmospheric dust into account to accurately determine dust optical properties. When atmospheric dust is not taken into consideration in satellite retrievals, errors may occur in analysis of the climate trends. A wide range of atmospheric and oceanic properties can be measured through remote sensing, using narrow band infrared spectral measurement from such instruments as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Very High Resolution Radiometer (AVHRR), the Atmospheric Infrared Sounder (AIRS), the High-Resolution Infrared Radiation Sounder (HIRS/2), and Geostationary Operational Environmental Satellites (GOES-8). 2 Key atmospheric properties such as atmospheric temperature profiles are determined based on measurements in IR region. For example, the presence of mineral dust can be determined using narrow band IR sensor channels centered near 8, 11, and 12 µm. 3 Abundant components of dust aerosol have characteristic peaks in the spectral region from 1099 to 1200 cm -1 which can be used for extracting information about atmospheric dust constituents. 2 1

2 Currently, Mie theory is used for modeling the optical properties. 4,5 However, Mie theory is derived for homogeneous spherical particles. Atmospheric mineral dust particles usually consist of inhomogeneous mixtures of particles and their aggregates and deviate from a spherical shape, which can impact the optical properties. The non-sphericity of mineral dust causes significant errors in calculations of its radiative effect. 6 Thus, the use of Mie theory in radiative transfer calculations and satellite retrieval algorithms could lead to large errors. For very small particles, where diameter of a particle is much smaller than the wave length (Rayleigh regime), simple analytical solutions have been derived for different particle shapes such as ellipsoids, disks, and needles. 7 A continuous distribution of ellipsoids (CDE) model is sometimes used to simulate absorption spectra for cosmic dust particles. 8 Hudson et al. 9,10 investigated particle shape effects on IR spectra for the most abundant mineral components of atmospheric aerosol. Experimental IR spectra were compared with simulation based on Mie theory and simple analytic models derived in the Rayleigh regime. The analytic solution results for characteristic particle shapes give consistently better agreement for resonance peak positions and band shapes, than Mie theory, derived for spherical particles. Specifically, it was found that the prominent Si- O stretch resonance absorptions for the silicate clays (illite, kaolinite, and montmorillonite) are better fit by assuming a disk shape. 9 For the non-clay mineral aerosol components (quartz, dolomite and calcite), it was found that a CDE model for particle shape gives better results. 10 These studies have tended to focus on single component mineral dust aerosol samples. Little, however, is known about the effect of composition and particle shape on IR absorption for more complex authentic dust. In this study we focus on IR extinction spectra of multicomponent authentic dust samples (Iowa loess and Saharan sand). Our goal is to investigate particle shape 2

3 effects on IR spectra for mineral dust and find ways to improve the accuracy of modeling atmospheric dust radiative transfer effects. 2. Methodology 2.1. Characterization of materials Scanning Electron Microscopy and energy dispersive X-ray spectroscopy The bulk elemental composition of each source material was determined using a Hitachi S- 3400N Scanning Electron Microscope (SEM) coupled with energy dispersive X-ray (EDX) spectrometer. Source materials were collected on carbon stub which was exposed to particles from the aerosol stream Mineralogy of complex samples Materials were obtained from Saharan desert and Loess Hills, Iowa. Iowa loess was used as received. Saharan sand was passed through a 100 um sieve prior to use. To determine the mineralogy of the complex samples (Iowa loess and Saharan sand), IR spectra of authentic dust were compared with IR spectra for mixtures of major components of mineral dust (quartz, amorphous silica, kaolinite, montmorillone, illite, calcite, dolomite, feldspars). IR spectra were measured for typical major components and authentic samples. Then spectra of components were added together in different proportions until they gave the best overlap with the spectra of mineral dust. The extinction spectra of mixtures were quantitatively compared to the experimental spectra of authentic sample through the use of χ 2. The χ 2 error is defined as the sum of the square of the difference between extinctions of the simulation and the experimental spectrum. The composition of the mixture which gives the best overlap with the experiment was taken as the composition of the sample. 3

4 2.2. Size distribution and FTIR extinction spectra To quantitatively investigate the optical properties of mineral dust, Multi-Analysis Aerosol Reactor System (MAARS), an instrument capable of simultaneously measuring aerosol size distributions and IR extinction spectra was used. The aerosol is generated from a suspension of powder in water with a constant output atomizer (TSI, Inc., Model 3076). Resulting aerosol passes through diffusion dryer (TSI Inc., Model 3062) where it is dried to a relative humidity below 10%. Then the dried aerosol is sent into an IR cell were we measure the infrared extinction spectra. Then the aerosol flow is split between a scanning mobility particle sizer (SMPS, TSI, Inc., Model 3936), and an aerodynamic particle sizer (APS, TSI, Inc., Model 3321) to measure full aerosol size distribution of the aerosolized compounds. SMPS measures an aerosol size distribution as a function of mobility diameter (D m ) and aerodynamic particle sizer (APS) measures a size distribution as a function of aerodynamic diameter (D a ). The aerodynamic diameter is related to the volume equivalent diameter (D ve ) by: (1) where χ is the dynamic shape factor, ρ o is the reference density (1 g cm 3 ), ρ p is the density of the particle, and C s (D a ) and C s (D ve ) are the Cunningham slip factors for the aerodynamic diameter and the volume equivalent diameter, respectively. The shape factor provides information about the shape of the particles (with χ =1 for a sphere). Similarly, the volume equivalent diameter can be related to the mobility diameter as follows: (2) 4

5 where C s (D m ) is the Cunningham slip factor for the mobility diameter. In order to determine the relationship between aerodynamic and mobility diameters, Equations (1) and (2) can be combined to give: / / (3) Equation (3) relates the measured mobility and aerodynamic particle diameters. Since both D m and D a are measured experimentally, and ρ p is known for the samples, Equation (3) can be used to empirically determine the dynamic shape factor χ. Once the SMPS and APS data have been combined on a common mobility diameter scale and χ is determined, the mobility diameter can be converted to a volume equivalent diameter using Equation (2) for use in the IR simulations. 11 The IR extinction spectra were measured using a FTIR spectrometer (Thermo Nicolet, Nexus Model 670). The IR spectra were acquired from cm -1 using 8 cm -1 resolution. The scan times for the IR, SMPS, and APS (210 sec) were synchronized for simultaneous measurement of the size distribution with the corresponding IR spectrum. The focus of this work is on the analysis of the IR resonance absorption features in the 800 to 1400 cm -1 spectral range, as most atmospheric dust constituents - clays, silicon oxides, carbonates and feldspars have characteristic peaks in this region Model simulations Measured full size distribution was combined with a set of optical constants available in a literature. Simulations of the extinction spectra were then carried out for different theoretical models including Mie theory and a model derived in the Rayleigh approximation for different characteristic particle shapes. 5

6 Mie theory is used to predict the modal optical properties of uniform spherical particles. Of particular importance are the angle-integrated extinction (C ext ), absorption (C abs ), and scatter cross-sections (C sca ), given by (4-6): 2 1,, (4) 2 1,, (5) (6) where the scattering coefficients a n (X,m) and b n (X,m) are expressed in terms of Ricatti Bessel functions that depend on the dimensionless size parameter X (where X= πnd/λ, D is the particle diameter, N is the refractive index of the surrounding medium with N 1 for air, and λ is the wavelength of the incident light) and the complex index of refraction m = n+ik. The simulation code used here calculates an extinction spectrum for a given measured particle size distribution and set of optical constants. The code essentially computes the extinction for a given size bin, and then sums over the size bins weighted by the particle number density in each bin to obtain the final spectrum. Simple analytic relations have been derived in the Rayleigh (small particle) limit to model resonance absorption cross sections for CDE, disk, and needle shaped particles. The analytic solutions for the absorption due to particles shapes based on CDE, disk, or needle models are given in equations (7) through (9): 7 (7) 2 (8) 1 (9) 6

7 where ε = ε +iε, is the complex dielectric constant, k =(2π/λ), and v is the volume of the particle, related to the diameter of an equivalent volume spherical particle by v =(πd 3 /6). Mie theory and shape solutions were then compared with experimentally measured extinction spectrum through the use of χ 2 error. 3. Results and Discussion The mineralogy of authentic dust samples was determined from IR spectra as described above. It was estimated that the clay content is approximately 88% and 93% for Iowa loess and Saharan sand, respectively. This is in a good agreement with the common fact that clays are one of the most important components of the fine fraction of mineral dust aerosol. The resulting spectra can be seen in Figure 1. Elemental composition of the authentic materials was then compared with elemental composition of resulting mixtures. The results are summarized in Table 1. Overall, the elemental analysis derived from IR fit is in a good agreement with elemental analysis determined by EDX spectrometry. As can be seen in Table 1, there are some errors associated with iron content. A possible reason for such disagreement can be due to the fact that common iron oxides and hydroxides (hematite, goethite) do not have strong extinction features in the IR spectral region extended from 800 to 1400 cm -1. The shape factors, experimentally determined for Iowa loess and Saharan sand from SMPS and APS data overlap are χ = 1.05±0.02 and 1.31±0.05 using average densities of ρ p = 1.43 and 2.30 g cm -3 for Iowa loess and Saharan sand, respectively. The shape factor results represent the mean values obtained from a series of experiments (~10 12) for each compound, and the errors reflect the standard deviation of the measurements. These results for Saharan dust are in a good agreement with Davies 12, who reported that sand particles composed of a mixture of different 7

8 minerals have a dynamic shape factor of The mean diameters are D w = 473±27, and 423±44 nm for Iowa loess and Saharan sand, respectively. As can be seen, within the IR resonance region of interest (~10 µm) these particles do not strictly satisfy the Rayleigh condition (D << λ). Figure 2 shows the comparison of the extinction spectrum for Iowa loess and Saharan sand aerosols with results from simulations for analytical solutions and Mie theory using the mineralogy determined from the fit shown in Figure 1. Optical constants for components of authentic samples were drawn from the published literature. 13,14,15 The top panel of Figure 2 shows the comparison between the experimental spectra and Mie simulations. Other plots shown in Figure 2 give the comparison between the experimental data and CDE, disks, and needles simulations for Iowa loess and Saharan sand. Only the resonance region from 800 to 1550 cm -1 is presented for each compound, showing both the Si O stretching region and the carbonate region. It is clear from the results in Figure 2 that the simple Rayleigh-based model fits the data much better than Mie theory, or the models for CDE or needle-like particles. Quantitative comparisons of the χ 2 differences between the experimental spectra and the model simulations confirm this conclusion. The Mie-based simulation shows a peak that is consistently blue shifted by ~ 40 cm -1 relative to the experimental spectrum. In general, the simulation based on Rayleigh theory provides a much better match to the experimental spectrum than the Mie theory simulation with respect to peak position and band shape. The peak maximum is now red shifted by less than 10 cm Atmospheric implications 8

9 As was mentioned previously, comparing difference measurements between specific narrow band IR sensor channels of MODIS (BT 8 BT 11 vs. BT 11 BT 12 ) could be used to indicate the presence of dust. Satellite retrievals could be adversely affected by using Mie theory for dust retrievals from narrow band satellite data, depending on the relative overlap between the actual and Mie predicted resonance peak placement and band shape. Figure 3 shows the integrated area ratio between the simulated and the experimental extinction signal integrated over narrowband channels BT 8, BT 11 and BT 12 for these four satellites. The closer the ratio of the integrated area of the shape simulation relative to the experiment is to one, the better the simulation. As can be seen in Figure 3, the shape simulations in general give better results. In the brightness temperature band centered at 8 µm (BT 8 ) using the HIRS/ µm ( cm -1 ) and MODIS µm ( cm -1 ) bands, there is only a small difference between integrated areas derived from Mie theory and shape simulations. With the exception of the BT 8 bands, there are dramatic differences between the experimental results and the Mie and shape simulations. Analytic solution simulations more closely approximate the integrated area relative to the experimental spectrum over channels BT 11 and BT 12. IR sensor channels centered at 11 and 12 µm are particularly important, since Sokolik 2 suggested using the cm -1 ( µm) spectral region as a determinant for dust. Mie theory appears to systematically underestimate the extinction in the BT 11 and BT 12 bands. The extinction data are inversely related to the brightness temperature (BT) data. Thus an underestimate in the BT 11 BT 12 difference signal in the extinction spectrum, corresponds to an overestimate in the BT spectrum. As a result, the BT 8 BT 11 brightness temperature difference signals will tend to be overpredicted in the Mie simulation. This further supports that Mie theory does not accurately model atmospheric mineral dust particles in the IR region. Thus, due care 9

10 must be taken when attempting to use Mie theory to derive compositional information from highresolution IR spectral data in the Si O stretch region. Spectral simulations based on analytic solutions might be preferable in modeling high-resolution spectra of mixed mineral dust aerosol obtained from field or satellite measurements, despite the fact that the Rayleigh condition (D << λ) is not strictly satisfied. 5. Conclusions Simultaneous size distributions and Fourier transform infrared extinction spectra have been measured for complex authentic samples, Iowa loess and Saharan sand. The measured size distributions are used in combination with published optical constants for simulations and their comparison to the measured extinction spectrum. It was found that for the samples studied here the Si O stretch resonance peak is blue shifted by more than 40 cm -1 for the Mie theory prediction. The combined analytic theory gives a better fit to the experimental resonance extinction spectra of Iowa loess and Saharan sand. Errors in the simulated peak position and line shape associated with Mie theory could adversely affect determination of mineral composition based on high-resolution IR satellite data. Systematic errors in the integrated absorbance for the major silicate resonance bands may affect radiative forcing calculations. The results from our study have been examined in the context of remote sensing measurements. It was shown that BT 11 BT 12 brightness temperature difference measurements are likely to be significantly overpredicted by Mie theory. Simple analytic models, derived in the small particle limit, may offer a better fit to the major band features for interpreting high-resolution spectra. Based on the results of this study it can be suggested that the accuracy of modeling atmospheric dust radiative transfer effects can be improved by using Rayleigh theory. 10

11 Table 1. Elemental composition of authentic dust samples and corresponding mixtures. Iowa loess Saharan sand IR fit EDX experiment IR fit EDX experiment Element Wt % Wt % Wt % Wt % Si Al Na Mg Fe K Ca Illite 56% Montmorillonite 32% Albite 10% Dolomite 2% Iowa Loess IL IR fit 916 Illite 89% Montmorillonite 3% Kaolinite 1% Silica 1% Albite 2% Dolomite 4% Saharan Sand SS IR fit Wavenumber (cm -1 ) Wavenumber (cm -1 ) Figure 1. Experimental infrared spectrum (black line) of Iowa loess (IL) and Saharan sand (SS) compared to IR fit (red line) in the Si O resonance region from 800 to 1600 cm -1. The compositions derived from IR for each sample are shown on the left. The underlined peak assignments correspond to the experimental spectrum. 11

12 Extinction (x10-3 ) Extinction (x10-3 ) Iowa Loess Mie Simulation 1043 Experiment χ 2 = 14.9* Analytical solution 1043 χ 2 = 2.64* Wavenumber (cm -1 ) Extinction (x10-3 ) Extinction (x10-3 ) Saharan Sand 2.0 Mie χ 2 = 6.23* Simulation Experiment 1039 Analytical solution 1039 χ 2 = 2.14* Wavenumber (cm -1 ) Figure 2. Experimental infrared spectrum of Iowa loess (left) and Saharan Sand (right) (black lines) compared to Mie and analytical solutions (red lines) in the Si O resonance region from 800 to 1500 cm -1. The underlined peak assignments correspond to the experimental spectrum. Integrated Area Ratio (Simulation/Experiment) IL BT 8 HIRS/2 HIRS/2 BT 11 ILSS IL SS IL SS SS ILSS ILSS IL MODIS MODIS AVHRR GOES-8 Mie Analytical solution Figure 3. The relative ratio of the integrated area for Mie (shaded bars) and analytic solutions (open bars) to the experimental spectrum for Iowa loess (IL) and Saharan sand (SS) for narrow band IR channels BT 8, BT 11 and BT 12 of HIRS/2, MODIS, AVHRR and GOES-8 sensors. The grey solid line indicates where the area of the simulation is equal to the experimental spectrum. SS MODIS BT 12 IL SS AVHRR IL SS GOES-8 12

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