Atmospheric aerosols can influence society and the environment in many ways including altering the planet's energy budget, the hydrologic cycle, and public health. However, the Fourth Assessment Report of the Intergovernmental Panel on Climate Change indicates that the anthropogenic radiative forcing associated with aerosol effects on clouds has the highest uncertainty in the future climate predictions. This thesis focuses on the nature of the organic fraction of ambient particles and how particles interact with clouds using a combination of tools including aircraft and ground measurements, models, and satellite data. Fine aerosol particles typically contain between 20 - 90% organic matter by mass and a major component of this fraction includes water soluble organic carbon (WSOC). Consequently, water-soluble organic species can strongly influence aerosol water-uptake and optical properties. However, the chemical composition of this fraction is not well-understood. PILS-TOC was used to characterize WSOC in ambient aerosol in Los Angeles, California. The spatial distribution of WSOC was found to be influenced by (i) a wide range of aerosol sources within this urban metropolitan area, (ii) transport of pollutants by the characteristic daytime sea breeze trajectory, (iii) topography, and (iv) secondary production during transport. Meteorology is linked with the strength of many of these various processes. Many methods and instruments have been used to study aerosol-cloud interactions. Each observational platform is characterized by different temporal/spatial resolutions and operational principles, and thus there are disagreements between different studies for the magnitude of mathematical constructs used to represent the strength of aerosol-cloud interactions. This work points to the sensitivity of the magnitude of aerosol-cloud interactions to cloud lifetime and spatial resolution of measurements and model simulations. Failure to account for above-cloud aerosol layers and wet scavenging are also shown to cause biases in the magnitude of aerosol-cloud interaction metrics. Air mass source origin and meteorology are also shown to be important factors that influence aerosol-cloud interactions. The results from this work contribute towards a better understanding of atmospheric aerosols and are meant to improve parameterizations that can be embedded in models that treat aerosol affects on clouds, precipitation, air quality, and public health.

Atmospheric fine particulate matter (PM2.5), which is mostly formed in the atmosphere from precursor gases, contributes to numerous environmental and health concerns. Quantifying the ambient concentrations of PM2.5 and precursor gases can be challenging. Hence, many scientific questions about the formation, chemical composition, and gas/particle partitioning of PM2.5 remain unanswered. Ambient Ion Monitor - Ion Chromatography (AIM-IC) was characterized and utilized to measure the water-soluble composition of PM2.5 (dominated by pNH4+, pSO42-, and pNO3-) and associated precursor gases (dominated by NH3(g), SO2(g), and HNO3(g)) during two field campaigns. The AIM-IC detection limits for hourly sampling were determined to be 3 - 45 ng m-3. The response time for “sticky” gases was significantly improved with a nylon denuder membrane. A novel inlet configuration for the AIM-IC, which minimizes sampling inlet losses and carryover in sample analyses, was implemented. Measurements from the BAQS-Met 2007 campaign were utilized to assess the accuracy of the AURAMS model and investigate gas/particle partitioning in SW Ontario. Due to high sulphate levels, NH3(g) was the limiting chemical factor in the formation and gas/particle partitioning of PM2.5. The errors in the predictions of relative humidity and free ammonia were responsible for the poor agreement iii between modelled and measured pNO3- values. The AIM-IC measurements from the CalNex 2010 study were compared to the CMAQ model and utilized to investigate the gas/particle partitioning in Bakersfield, CA. Very high NH3(g) concentrations were observed, and the formation and partitioning of PM2.5 was limited by HNO3(g) and H2SO4. Evidence of rapid removal of HNO3(g) by interactions with super-micron dust particles, and possibly with the alkaline surface was found. CMAQ exhibited significant biases in the predicted concentrations of pSO42-, NH3(g) and HNO3(g).

Atmospheric fine particulate matter (PM2.5), which is mostly formed in the atmosphere from precursor gases, contributes to numerous environmental and health concerns. Quantifying the ambient concentrations of PM2.5 and precursor gases can be challenging. Hence, many scientific questions about the formation, chemical composition, and gas/particle partitioning of PM2.5 remain unanswered. Ambient Ion Monitor - Ion Chromatography (AIM-IC) was characterized and utilized to measure the water-soluble composition of PM2.5 (dominated by pNH4+, pSO42-, and pNO3-) and associated precursor gases (dominated by NH3(g), SO2(g), and HNO3(g)) during two field campaigns. The AIM-IC detection limits for hourly sampling were determined to be 3 - 45 ng m-3. The response time for “sticky” gases was significantly improved with a nylon denuder membrane. A novel inlet configuration for the AIM-IC, which minimizes sampling inlet losses and carryover in sample analyses, was implemented. Measurements from the BAQS-Met 2007 campaign were utilized to assess the accuracy of the AURAMS model and investigate gas/particle partitioning in SW Ontario. Due to high sulphate levels, NH3(g) was the limiting chemical factor in the formation and gas/particle partitioning of PM2.5. The errors in the predictions of relative humidity and free ammonia were responsible for the poor agreement iii between modelled and measured pNO3- values. The AIM-IC measurements from the CalNex 2010 study were compared to the CMAQ model and utilized to investigate the gas/particle partitioning in Bakersfield, CA. Very high NH3(g) concentrations were observed, and the formation and partitioning of PM2.5 was limited by HNO3(g) and H2SO4. Evidence of rapid removal of HNO3(g) by interactions with super-micron dust particles, and possibly with the alkaline surface was found. CMAQ exhibited significant biases in the predicted concentrations of pSO42-, NH3(g) and HNO3(g).