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Atmospheric aerosols are the largest remaining uncertainty in the Earth’s radiative budget and it is important that we improve our knowledge of aerosol processes if we are to understand current radiative forcing and accurately project changes in future climate. Aerosols affect the radiation balance directly through the absorption and scattering of incoming solar radiation and indirectly through the modification of cloud microphysical properties. Understanding aerosol forcing remains challenging due to the short atmospheric residence time of aerosols resulting in large spatial and temporal heterogeneity in aerosol loading and chemical composition. Satellite retrievals are becoming increasingly important to improving our knowledge of aerosol forcing. They provide regular global data at finer spatial and temporal resolution than available through sparse groundbased point measurements or localised aircraft campaigns, but cannot unambiguously determine aerosol speciation, relying heavily on a priori assumptions. In this thesis I use data from two satellite instruments: the Along Track Scanning Radiometer 2 (ATSR-2) and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) interpreted using the Oxford-RAL Aerosol and Cloud (ORAC) retrieval scheme in three pieces of interrelated work. First I use satellite observations of aerosol optical depth a and cloud particle effective radius re from the ATSR-2 instrument in 1997 to investigate the Twomey indirect effect (IE, -δ ln re /δ ln τa) in regions of continental outflow. I generally find a negative correlation between τa and re with the strongest inverse relationships downwind of Africa. North America and eastern Asian continental outflow exhibits a strong seasonal dependence, as expected. Global values for IE range from 0.10 to 0.16, consistent with theoretical predictions. Downwind of Africa, I find that the IE is unphysically high but robust (r = −0.85) during JJA associated with high aerosol loading, and attribute this tentatively to the Twomey hypothesis accounting only for a limited number of physical properties of aerosols. Second, I test the response of the Oxford-RAL Aerosol and Cloud (ORAC) retrieval algorithm for MSG SEVIRI to changes in the aerosol properties used in the dust aerosol model, using data from the Dust Outflow and Deposition to the Ocean (DODO) flight campaign in August 2006. I find that using the observed DODO free tropospheric aerosol size distribution and refractive index compared with the dust aerosol properties from the Optical Properties of Aerosol and Cloud (OPAC) package, increases simulated top of the atmosphere radiance at 0.55 μm assuming a fixed aerosol optical depth of 0.5, by 10–15%, reaching a maximum difference at low solar zenith angles. This difference is sensitive to changes in AOD, increasing by ~2–4% between AOD of 0.4–0.6. I test the sensitivity of the retrieval to the vertical distribution of the aerosol and find that this is unimportant in determining simulated radiance at 0.55 μm. I also test the ability of the ORAC retrieval when used to produce the GlobAerosol dataset to correctly identify continental aerosol outflow from the African continent and I find that it poorly constrains aerosol speciation. I develop spatially and temporally resolved prior distributions of aerosols to inform the retrieval which incorporates five aerosol models: desert dust, maritime, biomass burning, urban and continental. I use a Saharan Dust Index and the GEOS-Chem chemistry transport model to describe dust and biomass burning aerosol outflow, and compare AOD using my speciation against the GlobAerosol retrieval during January and July 2006. I find AOD discrepancies of 0.2–1 over regions of biomass burning outflow, where AOD from my aerosol speciation and the GlobAerosol speciation can differ by as much as 50 - 70 %. Finally I use satellite observations of aerosol optical depth and cloud fraction from the MSG SEVIRI instrument to investigate the semi-direct effect of Saharan dust aerosol on marine stratocumulus cloud cover over the Atlantic during July 2006. I first use these data to study the spatial autocorrelation of aerosol optical depth and find that it is correlated over a lag of 0.1◦ (approximately 10 km at low latitudes), beyond which it rapidly decorrelates. I find a 15 % higher cloud fraction in regions with high dust loading (AOD > 0.5), compared with scenes with a lower dust loading (AOD < 0.5), which for high dust scenes increases with local static stability. I attribute this tentatively to aerosol solar shielding enhancing longwave cloud top radiative cooling which drives marine stratocumulus convection.

Organic particulate matter represents a significant fraction of sub-micrometer atmospheric aerosol mass. However, organic aerosol (OA) consists of thousands of different organic compounds making the simulation of its concentration, chemical evolution, physical and chemical properties extremely challenging. The identity of the great majority of these compounds remains unknown. The volatility of atmospheric OA is one of its most important physical properties since it determines the partitioning of these organic compounds between the gas and particulate phases. The use of lumped compounds with averaged properties is a promising solution for the representation of OA in atmospheric chemical transport models. The two-dimensional volatility basis set (2D-VBS) is a proposed method used to describe OA distribution as a function of the volatility and oxygen content of the corresponding compounds. In the first part of the work we evaluate our ability to measure the OA volatility distribution using a thermodenuder (TD). We use a new method combining forward modeling, introduction of ‘experimental’ error and inverse modeling with error minimization for the interpretation of TD measurements. The OA volatility distribution, its effective vaporization enthalpy, the mass accommodation coefficient and the corresponding uncertainty ranges are calculated. Our results indicate that existing TD-based approaches quite often cannot estimate reliably the OA volatility distribution, leading to large uncertainties, since there are many different combinations of the three properties that can lead to similar thermograms. We propose an improved experimental approach combining TD and isothermal dilution measurements. We evaluate this experimental approach using the same model and show that it is suitable for studies of OA volatility in the lab and the field. Measurements combining a thermodenuder (TD) and a High Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) took place during summer and winter in Paris, France as part of the collaborative project MEGAPOLI and during the winter of 2013 in the city of Athens. The above volatility estimation method with the uncertainty estimation algorithm is applied to these datasets in order to estimate the volatility distribution for the organic aerosol (OA) and its components during the two campaigns. The concentrations of the OA components as a function of temperature were measured combining data from the thermodenuder and the aerosol mass spectrometer (AMS) with Positive Matrix Factorization (PMF) analysis. Combining the bulk average O:C ratios and volatility distributions of the various factors, our results are placed into the two-dimensional volatility basis set (2D-VBS) framework. The OA factors cover a broad spectrum of volatilities with no direct link between the average volatility and average O:C of the OA components. An intercomparison among the OA components of both campaigns and their physical properties is also presented. The approach combining thermodenuder and isothermal dilution measurements is tested in smog chamber experiments using OA produced during meat charbroiling. The OA mass fraction remaining is measured as a function of temperature in the TD and as a function of time in the isothermal dilution chamber. These two sets of measurements are used together to estimate the volatility distribution of the OA and its effective vaporization enthalpy and accommodation coefficient. In the isothermal dilution experiments approximately 20% of the OA evaporate within 15 min. In the TD almost all the OA evaporated at approximately 200oC. The resulting volatility distributions suggest that around 60-75% of the cooking OA (COA) at concentrations around 500 μg m-3 consists of low volatility organic compounds (LVOCs), 20-30% of semi-volatile organic compounds (SVOCs) and around 10% of intermediate volatility organic compounds (IVOCs). The estimated effective vaporization enthalpy of COA is 100 ± 20 kJ mol-1 and the effective accommodation coefficient is around 0.05. The characteristics of the COA factor from the Athens campaign are compared to those of the OA produced from meat charbroiling in these experiments. In the next step, different parameterizations of the organic aerosol (OA) formation and evolution in the two-dimensional Volatility Basis Set (2D-VBS) framework are evaluated using ground and airborne measurements collected in the 2012 Pan-European Gas AeroSOls-climate-interaction Study (PEGASOS) field campaign in the Po Valley, Italy. A number of chemical schemes are examined, taking into account various functionalization and fragmentation pathways for biogenic and anthropogenic OA components. Model predictions and measurements, both at the ground and aloft, indicate a relatively oxidized OA with little average diurnal variation. Total OA concentration and O:C ratios were reproduced within experimental error by a number of chemical aging schemes. Anthropogenic SOA is predicted to contribute 15-25% of the total OA, while SOA from intermediate volatility compounds oxidation another 20-35%. Biogenic SOA contributions varied from 15 to 45% depending on the modeling scheme. The average OA and O:C diurnal variation and their vertical profiles showed a surprisingly modest sensitivity to the assumed vaporization enthalpy for all aging schemes. This can be explained by the intricate interplay between the changes in partitioning of the semivolatile compounds and their gas-phase chemical aging reactions. The same set of different parameterizations of the organic aerosol (OA) formation and evolution in the two-dimensional Volatility Basis Set (2D-VBS) framework are evaluated using ground measurements collected in the 2013 PEGASOS field campaign in the boreal forest station of Hyytiälä in Southern Finland. The most successful is the simple functionalization scheme of Murphy et al. (2012) while all seven aging schemes have satisfactory results, consistent with the ground measurements. Despite their differences, these schemes predict similar contributions of the various OA sources and formation pathways. Anthropogenic SOA is predicted to contribute 11- 18% of the total OA, while SOA from intermediate volatility compounds oxidation another 18- 27%. The highest contribution comes from biogenic SOA, as expected contributing 40 to 63% depending on the modeling scheme. The primary OA contributes 4% while the SOA resulting from the oxidation of the evaporated POA varies between 4 to 6%. Finally, 5-6% is according to the model the results of long range transport from outside the modeling domain.

Most current models for the deposition of aerosol particles in the human lung are based on a Lagrangian reference frame, which is ill-suited for modeling transient eects. Deposition models based on an Eulerian reference frame are much better at capturing instantaneous time-dependent eects, though they are dicult to create. In the interest of developing such models, mathematical techniques were used to describe the velocity elds of simple particle ows. Analytic expressions describing the time-dependent ow of particles through a curved pipe were created and implemented numerically. The numerical simulations were used to determine which ow regimes required the use of Eulerian modelling for deposition prediction.

This work investigates the effects of anthropogenic aerosols on deep convective clouds and the associated radiative forcing in the Houston area. The Goddard Cumulus Ensemble model (GCE) coupled with a spectral-bin microphysics is employed to investigate the aerosol effects on clouds and precipitation. First, aerosol indirect effects on clouds are separately investigated under different aerosol compositions, concentrations and size distributions. Then, an updated GCE model coupled with the radiative transfer and land surface processes is employed to investigate the aerosol radiative effects on deep convective clouds. The cloud microphysical and macrophysical properties change considerably with the aerosol properties. With varying the aerosol composition from only (NH4)2SO4, (NH4)2SO4 with soluble organics, to (NH4)2SO4 with slightly soluble organics, the number of activated aerosols decreases gradually, leading to a decrease in the cloud droplet number concentration (CDNC) and an increase in the droplet size. Ice processes are more sensitive to the changes of aerosol chemical properties than the warm rain processes. The most noticeable effect of increasing aerosol number concentrations is an increase of CDNC and cloud water content but a decrease in droplet size. It is indicated that the aerosol indirect effect on deep convection is more pronounced in relatively clean air than in heavily polluted air. The aerosol effects on clouds are strongly dependent on RH: the effect is very significant in humid air. Aerosol radiative effects (ARE) on clouds are very pronounced for mid-visible single-scattering albedo (SSA) of 0.85. Relative to the case without the ARE, cloud fraction and optical depth decrease by about 18% and 20%, respectively. The daytime-mean direct forcing is about 2.2 W m-2 at the TOA and -17.4 W m-2 at the surface. The semi-direct forcing is positive, about 10 and 11.2 W m-2 at the TOA and surface, respectively. Aerosol direct and semi-direct effects are very sensitive to SSA. The cloud fraction, optical depth, convective strength, and precipitation decrease with the increase of absorption, resulting from a more stable atmosphere due to enhanced surface cooling and atmospheric heating.

A single-ultrafine-particle mass spectrometer was constructed and deployed for size-resolved ultrafine aerosol composition measurements during the winter of 2002-2003 in College Station, Texas. Three separate experiments were held between December and March with six week intervals. Almost 128,000 mass spectra, corresponding to particles with aerodynamic diameters between 35 and 300 nm, were collected and classified. Fifteen statistically significant classes were identified and are discussed in this paper. Nitrate, potassium, carbon, and silicon/silicon oxide were the most frequently observed ions. Nitrate was present in most of the particles, probably due to the agricultural activity in the vicinity of the sampling site. The nitrate detection frequency was found to be sensitive to the ambient temperature and relative humidity. Another particle class, identified as an amine, exhibited strong relative humidity dependence, appearing only during periods of low relative humidity. There is evidence that some of the detected particles originated from the large urban centers, and were coated with nitrate, sulfate, and organics during transport.

The recent development of satellite observations of limb scattered sunlight at optical wavelengths has afforded a new opportunity to measure the vertical structure of atmospheric composition from the upper troposphere to the mesosphere, on a global scale. The determination of profiles of atmospheric composition from observed limb radiance profiles requires two elements, a forward radiative transfer model and a species specific inversion algorithm. In this work, the development of a new, fully spherical, successive orders radiative transfer model, SASKTRAN, for the analysis of limb scattered sunlight is presented. The model is incorporated into a novel relaxation algorithm that employs spectral ratios to retrieve profiles of stratospheric aerosols from limb radiance measurements collected by the Canadian OSIRIS instrument on the Odin satellite.<p>The SASKTRAN forward model results compare favorably with both OSIRIS observations as well as with other radiative transfer model calculations while remaining computationally practical for the operational inversion of large satellite data sets.<p>The spectral ratio relaxation algorithm is able to retrieve aerosol number density profiles at stratospheric altitudes from limb radiance profiles assuming the height profile of the aerosol particle size distribution is known. The equivalent aerosol extinction derived from the OSIRIS measurements at visible wavelengths agrees with coincident occultation measurements from other satellite instrumentation to within 15% when a size distribution appropriate for background aerosol conditions is used. Finally, it is demonstrated that the incorporation of simultaneous infra-red observations at 1530 nm into the inversion yields a useful proxy for the aerosol size distribution parameters.

Administration of medication directly to diseased lungs reduces adverse systemic side effects. For cystic fibrosis, jet nebulizers are the standard aerosol delivery system since they can aerosolize drugs that require relatively large volumes of liquid. Selection of the appropriate nebulizer for a given drug is crucial to ensure delivery of the therapeutic dose. This selection, ideally, requires knowledge of the pulmonary drug deposition (PDD). The gold standard for accurately measuring PDD is nuclear medicine techniques, which exposes the subject to radiation and therefore cannot be used repeatedly to test multiple devices. An alternative is to characterize the nebulizer using in vitro experiments and estimate the device’s in vivo performance. However these techniques are time-consuming and can only collect data for one breathing pattern and drug-device combination. Therefore this study is to formulate mathematical models for jet nebulizers that can estimate PDD based on the drug-device combination and patient’s breathing patterns.