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Aerosol refractive index (RI) is related to particle composition and density, is used in optical spectroscopy studies to probe aerosol physiochemical properties during chemical reactions and gas-particle partitioning, and is important in atmospheric physics. Indeed, aerosol radiative forcing calculations require accurate descriptions of the real (n) and imaginary (k) RI components and their dependence on wavelength, humidity and particle mixing state. Using cavity ring-down spectroscopy (CRDS) and photoacoustic spectroscopy (PAS) to measure the extinction and absorption cross sections for mobility-selected aerosols is recognised as a good approach to retrieving n and k accurately. However, little work has assessed rigorously the sensitivity and accuracy of the retrieved values from this approach. This work investigates RI retrievals from CRDS- and PAS-measured optical properties for mobility-selected aerosols composed of ammonium sulfate (non-absorbing), nigrosin (strongly light absorbing) or a mixture of these two species. Importantly, we assess the sensitivity in our RI retrievals and then apply a Monte Carlo error propagation analysis to quantify the retrieval accuracy. Our Monte Carlo analysis is the first to account for the full range of uncertainties involved in RI retrievals from optical measurements on mobility-selected aerosol. We also report the first experimental validation of predictive RI mixing rules for non-aqueous internally mixed light absorbing aerosols by comparing mixing rule predictions with measurements for aerosol composed of internal mixtures of ammonium sulfate and nigrosin. The commonplace volume fraction mixing rule fails to predict refractive indices accurately and mixing rules with a physical basis must be used.

Photoacoustic spectroscopy is the technique-of-choice for non-contact and in situ measurements of light absorption coefficients for aerosols. For most aerosol photoacoustic (PA) detectors, a key process is the amplification of the acoustic pressure wave generated from light absorption through excitation of a pressure eigenmode of a PA cell. To our knowledge, no modeling of the acoustics, sensitivity or signal-to-background ratio (SBR) has been performed for the PA cells applied commonly to aerosol absorption measurements. In this Part 1 manuscript, we develop a finite element method (FEM) framework to simulate the acoustic response and SBR of photoacoustic cells. Furthermore, we validate this modeling framework by comparing FEM predictions of single-resonator PA cells with measurements using a prototype single-resonator cell, the geometry of which can be readily adjusted. Indeed, single-resonator cells are applied commonly to aerosol absorption measurements. We show that our model predicts accurately the trends in acoustic properties with changes to cell geometry. We investigate how common geometric features, used to suppress detection of background and noise processes, impact on the SBR of single-resonator PA cells. Such features include using multiple acoustic buffer volumes and tunable air columns. The FEM model and measurements described in this article provide the foundation of a companion paper that reports the acoustic properties and optimization of a two-resonator PA cell used commonly in aerosol research.

Biases in absorption coefficients measured using a filterbased absorption photometer (Tricolor Absorption Photometer, or TAP) at wavelengths of 467, 528 and 652 nm are evaluated by comparing to measurements made using photoacoustic spectroscopy (PAS). We report comparisons for ambient sampling covering a range of aerosol types including urban, fresh biomass burning and aged biomass burning.Data are also used to evaluate the performance of three different TAP correction schemes. We found that photoacoustic and filter-based measurements were well correlated, but filter-based measurements generally overestimated absorption by up to 45 %. Biases varied with wavelength and de-pended on the correction scheme applied. Optimal agreement to PAS data was achieved by processing the filter-based measurements using the recently developed correction scheme of Müller et al. (2014), which consistently reduced biases to 0 %–18 % at all wavelengths. The biases were found to be a function of the ratio of organic aerosol mass to light-absorbing carbon mass, although applying the Müller et al. (2014) correction scheme to filter-based absorption measurements reduced the biases and the strength of this correlation significantly. Filter-based absorption measurement biases led to aerosol single-scattering albedos that were biased low by values in the range 0.00–0.07 and absorption Ångström exponents (AAEs) that were in error by±(0.03–0.54). The discrepancy between the filter-based and PAS absorption measurements is lower than reported in some earlier studies and points to a strong dependence of filter-based measurement accuracy on aerosol source type.