Presented by: Robyn D. Williams EAS 6410 April 19, 2004 Assessing Optical, Thermodynamic and Physical Properties of Aerosols By Simulating LIDAR Response Presented by: Robyn D. Williams EAS 6410 April 19, 2004

Objectives To explore how thermodynamic and physical aerosol properties affect optical properties To simulate LIDAR measurements by coupling thermodynamic and light scattering models To determine if LIDAR measurements can be used to relate aerosol properties

Background

Aerosols Particulate Matter Sources Large Variability Natural Anthropogenic Large Variability Small spatial and temporal scales An aerosol is defined as particulate matter suspended in a fluid medium that typically range in sizes up to approximately 10nm They have direct and indirect affect on climate Sources Natural: Dust, sea spray, volcanic action, biomass burning Anthropogenic: fuel combustion, transportation sources, industrial processes o    Due to the large array or sources, various gas to particle conversion processes, and relative short residence times, tropospheric aerosols vary widely in size and composition over small spatial and temporal scales.

Estimated Aerosol Properties Ground Based Measurement Dynamic Atmosphere Continuous Measurement LIDAR Signal Transmission Particle/ Signal Interaction Signal Backscatter Backscattered Signal Analysis ground based measurements are ideal to aid in characterizing tropospheric aerosols because their continuous measurements can relate how aerosol properties change with the dynamic atmospheric conditions Light and Detection and ranging – is a ground based instrument that sends out a signal to a particle and then analyzes the signal that is scattered back from the target. Changes in the properties of the signal can tell a lot about the properties of the target

Aerosol Optical Properties Factors Particle size Wavelength Refractive Index/ Composition Particle Hygroscopity Figure shows four of the major interactions of light and particles Scattering and absorption of light by a particle is governed by particle size, wavelengh, and composition The refractive index is a function of composition - The ratio of the speed of light in vacuum to the speed of light in the material. Particle Hygroscopity or ability to take up water affects the particle size and composition

Mie Theory Relates light scattering governing factors Assumes homogenous spherical geometry Valid domain: It is valid in regions where the particle is approx. equal to the incident wavelength

Phase Function Angular Distribution of the Intensity Ratio of scattered intensity at a particular angle to total the scattered intensity at all angles LIDAR backscattering, =180º Describes the scattering intensity as a function of angle Since the LIDAR is a ground bases measurement – we consider back scattering at an 180 degree angle

Thermodynamics of Aerosols Water affects both the size and composition of aerosol Water Uptake = f(Relative Humidity) Deliquesence Relative Humidity Spontaneous particle growth Inorganic salts Chemical Equilibrium As previously stated – a particles ability to uptake water affects the particle size and composition The amount of water uptake depends on the relative humidity As more of the inorganic salts begin to dissolve and water uptake increases the concentration of the aerosol is diluted and the composition and refractive index is affected The water uptake process is governed by chemical equilibrium

Thermodynamics of Aerosols This diagram shows how once the deliquesence relative humidity is reached – the particle size grows exponentially Source: Pandis et al (1995)

LIDAR Simulation

Simulation Schematic A thermodyamic model, ISORROPIA developed by Thanos Nenes et al and a light scattering interface MIETAB developed by Augustus Miller

Aerosol Type Two aerosol types: urban and rural were used to consider lidar’s backscatter’s dependence on aerosol type/composition

Size Distributions The size distribution for rural and urban particles are tri modal The respective number distributions for each aerosol types were divided into three modes: fine, accumulation, and course Each mode was then separated into 11-14 bins

Simulation Schematic Next, the median diameters from each bin along with the typical aerosol compostion was inputed into the thermodynamic model, ISORROPIA The thermodynamic model then calculated the water uptake, corresponding wet diameter, and refractive index as a function of relative humidity Three relative humidities, 30%,60%,90% representing dry, semi-wet, and wet were isolated for each diameter The corresponding wet diameter and refractive index were then entered into MieTab along with a constant wavelength of 0.55 microns 0.55 microns represents the median/peak value of the sun’s concentrated radiation region Lastly, Mietab provided the phase function as a for angles from 0 to 180 for each input combination

Results & Discussion

Backscatter vs. Dry Particle Size In the essence of time only a subset of the results will be shown This graph represents the trend seen by all modes – with an exception of the mode three- the size parameters were large in this mode which pushes the scattering into the geometric scattering regime which exhibits lots of scatter As expected the results show that as a particle gets larger the lower the backscatter signal. This happens because as a particle scatters more forward light as its size increases As the relative humidity increases the greater the magnitude of backscatter decrease, because the particles uptake more water as the relative humidity increases

Backscatter vs. Relative Humidity As previously stated the backscatter decreases as the relative humidity increases

Backscatter vs. Aerosol Type The backscatter of urban mode II and rural mode II were compared in attempt to have relative constant particle size. The log-normal distribution mean diameter is approximately the same for both of these modes As you can see the backscatter curves lie on top of each other at each relative humidity This occurs because the composition differences in the urban and rural particles are not distinct enough to have a impact on the backscatter

Conclusions Good Simulation Relative Humidity/Particle Hygroscopity affects: Physical Properties/Composition Optical Properties Inconclusive aerosol type/composition effect The coupled thermodynamic and light scattering appears to provides a good representation of LIDAR measurements From this simulation it can be seen that the relative humidity had the most prominent effect – the simulation showed its effect on physical properties greatly affects the optical properties The composition effect could not be determined from the simulation because of the similar nature of the aerosols considered

Conclusions Backscatter vs. Dry Aerosol size and Backscatter vs. Relative Humidity Trends can possibly be correlated by a power or exponential model Based on shapes of curve there may be a possibility that quantification of the relationship between backscatter and other properties may be achieved by using some type of numerical method such as Gauss-Newton Method – However, this was beyond scope of project

Alternate Simulations Aerosol Size Vary Wavelength Aerosol Type/Composition Same Diameters Vary Composition Distinct Aerosol Types Vary Modal Composition Determine Backscatter To improve the simulation, Since the relative size between wavelength and particle size is so important The wavelength should be varied and this could possibly provide a method of simulating how two lidars emitting different wavelength signals can estimate particle size Aerosol type and composition can also be better analyzed by choosing the same diameter sizes and entering compositions of more distinct aerosols – such as marine aerosol which contains more sodium chloride A better estimate would also be obtained if the composition were varied from mode to mode – because different species are different sizes therefore existing in different modes Than he backscatter can be determined as more of a function of compostion

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