Aerosols, or particulate matter (PM), can affect climate through scattering and absorption of radiation and influence the radiative properties, precipitation efficiency, thickness, and lifetime of clouds. Aerosols are one of the greatest sources of uncertainty in climate model predictions of radiative forcing. To fully understand the sources of uncertainty contributing to the radiative properties of aerosols, measurements of PM mass, composition, and size distribution are needed globally and seasonally. To add to the current understanding of the seasonal and temporal variations in aerosol composition and chemistry, this study has focused on the quantification, speciation, and characterization of atmospheric PM in urban and rural regions of the United States (US) for short and long periods of time. In the first two chapters, we focus on 1 month of aerosol and gas-phase measurements taken in Fresno, CA, an urban and agricultural area, during the National Aeronautics and Space Administration's (NASA) field study called DISCOVER-AQ. This air quality measurement supersite included a plethora of highly detailed chemical measurements of aerosols and gases, which were made at the same time as similar aircraft column measurements of aerosols and gases. The goal of DISCOVER-AQ is to improve the interpretation of satellite observations to approximate surface conditions relating to air quality, which can be achieved by making concurrent ground- and aircraft-based measurements of aerosols and gases. We begin in chapter 2 by exploring the urban aerosol and gas-phase dataset from the NASA DISCOVER-AQ study in California. Specifically, we discuss the chemical composition and mass concentration of water-soluble PM2.5 that were measured using a particle-into-liquid sampler with ion chromatography (PILS-IC) in Fresno, California from January 13–February 10, 2013. This data was analyzed for ionic inorganic species, organic acids and amines. Gas-phase species including HNO3 and NH3 were collected with annular denuders and analyzed using ion chromatography. Using the thermodynamic E-AIM model, inorganic particle water mass concentration and pH were calculated for the first time in this area. Organic particle water mass concentration was calculated from [kappa]-Köhler theory. In chapter 3 further analysis of the aerosol- and gas-phase data measured during DISCOVER-AQ was performed to determine the effectiveness of a local residential wood burning curtailment program in improving air quality. Using aerosol speciation and concentration measurements from the 2013 winter DISCOVER-AQ study in Fresno, CA, we investigate the impact of residential wood burning restrictions on fine particulate mass concentration and composition. Key species associated with biomass burning in this region include K+, acetonitrile, black carbon, and biomass burning organic aerosol (BBOA), which represents primary organic aerosol associated with residential wood burning. Reductions in acetonitrile associated with wood burning restrictions even at night were not observed and most likely associated with stagnant conditions during curtailment periods that led to the buildup of this long-lived gas. In chapter 4 we transition to the rural aerosol dataset from the DOE SGP site. We discuss the chemical composition and mass concentration of non-refractory submicron aerosols (NR-PM1) that were measured with an aerosol chemical speciation monitor (ACSM) at the DOE SGP site from November 2010 through June 2012. Positive matrix factorization (PMF) was performed on the measured organic aerosol (OA) mass spectral matrix using a newly developed rolling window technique to derive factors associated with distinct sources, evolution processes, and physiochemical properties. The rolling window approach captured the dynamic variations of the chemical properties of the OA factors over time. Three OA factors were obtained including two oxygenated OA (OOA) factors, differing in degrees of oxidation, and a BBOA factor. Sources of NR-PM1 species at the SGP site were determined from back trajectory analyses. NR-PM1 mass concentration was dominated by organics for the majority of the study with the exception of winter, when NH4N33 increased due to transport of precursor species from surrounding urban and agricultural regions and also due to cooler temperatures. Chapter 5 is a continuation of chapter 4, where we will explore the use of the multilinear engine (ME-2) as a factor analysis technique, which is an algorithm used for solving the bilinear model called positive matrix factorization (PMF). The importance of ME-2 and its potential application on the long-term aerosol chemical speciation monitor (ACSM) data collected from the Department of Energy (DOE) Southern Great Plains (SPG) site will be discussed. ME-2 was performed on 19 months of OA mass spectral data obtained from the ACSM at the SGP site. Evaluation of ME-2 results are presented, followed by comparison of ME-2 factor results with corresponding OACOMP factor results reported in chapter 4. We show that ME-2 can determine a biomass burning organic aerosol (BBOA) factor during periods when OACOMP cannot. (Abstract shortened by ProQuest.)