Satoshi Takahama

Water content of ambient aerosol during the Pittsburgh Air Quality Study

The aerosol water content and volumetric growth factors of fine particulate matter were measured during July-August 2001 and January-June 2002 in an urban park about 6 km from downtown Pittsburgh, Pennsylvania. Most of the aerosol during the study was transported to the region from other areas, and its composition and concentration were characteristic of the regional particulate matter in the northeastern United States. During the summer months the ambient aerosol practically always contained water even when the relative humidity ( RH) was as low as 30%. In contrast, during the winter the aerosol was dry below 60% RH. The spring months were characterized by a transitional behavior between these two states. The observed seasonal behavior can be explained by the aerosol acidity. The summer aerosol was acidic and retained water at low RH. The winter aerosol was neutral and became wet when the relative humidity reached the deliquescence point of ammonium nitrate. The observations during July 2001 were compared with the predictions of the thermodynamic Gibbs Free Energy Minimization (GFEMN) model and the aerosol inorganics model ( AIM), neglecting the organic aerosol contribution to water absorption. The models under-predicted water concentrations by about 35%, but no clear correlation between organic mass and the excess water was observed. On average, the contribution of the organics to water absorption appeared to be higher during the afternoon hours and when the aerosol was presumably more oxidized.

Modeling the diurnal variation of nitrate during the Pittsburgh Air Quality Study

A thermodynamic model, the Gibbs Free-Energy Minimization model (GFEMN), was used to simulate the partitioning of PM2.5 nitrate aerosol and nitric acid using highly time-resolved inorganic measurements collected at the Pittsburgh Air Quality Study during July 2001 and January 2002. Model results were evaluated using independent, high time resolution measurements of aerosol nitrate. The mean observed concentration in July was 0.6 mug/m(3) and 2.1 mug/m(3) in January. Model predictions were in agreement with the observations within 0.5 mug/m(3) on average, with measurement uncertainties often accounting for these discrepancies. The simulations were run assuming particles were liquid in July for all relative humidities (RHs) and solid below 60% RH in January. For both seasons the assumed physical state did not influence considerably the overall agreement with observations. The assumption of particle mixing state did appear to influence model error; however, assuming that particles were externally mixed during low RH periods in July improved agreement significantly. The exceptional sensitivity of predicted aerosol nitrate to ammonia in western Pennsylvania suggests that reductions in PM2.5 may be assisted by reductions in ammonia emissions.

Coatings and clusters of carboxylic acids in carbon-containing atmospheric particles from spectromicroscopy and their implications for cloud-nucleating and optical properties

Particle shape and distribution of chemical compounds within individual particles are implied in the parameterizations used in air quality and climate models for radiative transfer, volatility, and mass transfer. In this study we employ Scanning Transmission X-Ray Microscopy with Near-Edge X-Ray Absorption Fine Structure Spectroscopy with image analysis and pattern recognition techniques to characterize the chemical structure of 636 particles collected on six field campaigns in the western hemisphere between 2004 and 2008. Many of the particles were chemically heterogeneous. A few observed types include black carbon particles covered by aqueous-phase components (n = 90), dust particles with organic clumps (106), organic particles enriched in carboxylic acid at the surface (54), and inorganic cores encapsulated by organic shells (10). The 90 particles in the first category collectively contained 95 regions showing a strong black-carbon spectral signature associated with the aqueous-phase components, of which 78 were between 0.1 and 1 mm. Organic mass fraction of the organic dust particles varied significantly (mean and standard deviation of 0.3 +/- 0.2), and over half of these dust particles were found to be nearly spherical. Thickness of acid-enriched coatings and carbon on inorganic cores were less than 0.6 mu m in most cases, but accounted for < 0.01 to 0.98 of the particle volume fraction. More than half of the identified organic particles (359) were found to be chemically heterogeneous, and 32 particles were observed as agglomerations or inclusions but did not meet one or more of the criteria of the categories described here. The acidic coatings on black carbon are calculated to have a significant impact on the critical supersaturation of these particles. The measured distribution of aspect ratios of dust and other particles in our samples ranged nonuniformly between 1.0 and 4.6 with a mean of 1.4, which can affect assessment of extinction-to-backscatter ratios over the case where spherical geometry is assumed.

Simulation of the thermodynamics and removal processes in the sulfate‐ammonia‐nitric acid system during winter: Implications for PM2.5 control strategies

Reference EPFL-ARTICLE-175723doi:10.1029/2004JD005038View record in Web of Science Record created on 2012-03-15, modified on 2016-12-14

Quantification of Carboxylic and Carbonyl Functional Groups in Organic Aerosol Infrared Absorbance Spectra

Atmospheric aerosols are one of the least understood components of the climate system and incur adverse health effects on susceptible populations. Organic aerosols can make up as much as 80% of atmospheric aerosols (Lim and Turpin 2002), and so its quantification and characterization plays an important role in reducing our uncertainty with regards to aerosol impacts on health and climate. As the number of organic molecules in the atmosphere are diverse in number (Hamilton et al. 2004), we advance a functional group representation of organic molecules as measured by Fourier transform infrared spectroscopy (FTIR) to characterize the chemical composition of particle samples. This study describes and evaluates the algorithm introduced by Russell et al. (2009) for apportionment and quantification of oxygenated (carbonyl and hydroxyl) functional groups from infrared absorption spectra. Molar absorptivities for carbonyl and hydroxyl bonds in carboxylic groups are obtained for several dicarboxylic compounds, and applied to a multifunctional compound and mixture to demonstrate the applicability of this method for more complex samples. Furthermore, functional group abundances of two aldehydic compounds, 2-deoxy-d-ribose and glyceraldehyde, atomized from aqueous solution are in quantitative agreement with number of bonds predicted after transformation of these compounds into diols. The procedure for spectra interpretation and quantitative analysis is described through the context of an algorithm in which contributions of background and analyte absorption to the infrared spectrum are apportioned by the superposition of lineshapes constrained by laboratory measurements. Copyright 2013 American Association for Aerosol Research

A molecular dynamics study of water mass accommodation on condensed phase water coated by fatty acid monolayers

As the water uptake by particles and clouds influences the radiative balance of the Earth, it is desirable to understand the mechanisms and parameters, which regulate water uptake in these colloidal particles. In this work, molecular dynamics simulations were used to simulate scattering or accommodation of water vapor molecules impinging on a slab of water and slabs of water coated by monomolecular amphiphile films: octanoic acid (C-8) at surface densities of 29 and 18 angstrom(2) per molecule and myristic acid (C-14) at 29 angstrom(2) per molecule. The mass accommodation coefficient of near unity on a pure water slab is in agreement with values estimated using similar scattering simulations using other potentials for water. The addition of surface-active organic molecules in quantities corresponding to less than 1% of mass in a typical cloud droplet are predicted to reduce this mass accommodation coefficient by 70-100% in similar types of scattering simulations. The mass accommodation coefficient decreased monotonically with projected surface coverage of the hydrocarbon backbones, although the accommodation mechanisms differed by packing density and type of organic molecule. The mechanisms of interaction of the impinging water vapor molecules with the simulated organic films are discussed in the context of their chemical characteristics and physical structures (e.g., fatty acid chain orientation).

Single‐particle oxidation state and morphology of atmospheric iron aerosols

Sixty-three iron-containing particles from five field campaigns (PELTI, ACE-Asia, MILAGRO (urban location and aloft on NCAR C-130), INTEX-B) were characterized with scanning transmission X-ray microscopy and near-edge X-Ray absorption fine structure L-edge spectroscopy (NEXAFS-STXM). Particle sizes ranged from 0.2 to 4.5 mu m and were found in many types of morphologies. Iron was found to exist in many different oxidation states with average Fe(II) to Fe( II) + Fe(III) ratios ranging from 0.0 to 0.73. Twenty-two inclusions and six agglomerations were found. For 29 particles, concurrent (spatially resolved) carbon K-edge absorption spectra were collected; X-ray images suggest that in some instances, there are clear phase barriers between iron and carbonaceous regions in agglomerations and irregular particles. Occurrences of Fe(II) fractions and organic functional group abundances among particles appeared primarily in two clusters, one group high in both values and the other low in both, though consistent correlations between the two variables within each particle were not observed. The reduced form of iron on particle surfaces was observed in 16 particles, possibly suggesting atmospheric processing. For this limited set of particles, neither inferred particle source nor surface processing was by itself a strong predictor of overall Fe(II) fraction, indicating that both are important for variables contributing to the observed Fe(II) fraction in the atmosphere. In addition, seven spherical particles from the ACE-Asia campaign showed an iron shell with an absence of iron toward the core. These particles have carbon spectra characteristics consistent with tarballs described by Posfai et al. (2004), Hand et al. (2005), and Tivanski et al. (2007), which were previously identified as homogeneous carbon spherules. In this work, NEXAFS-STXM has detected heterogeneities in iron distribution and redox state over individual particles, showing the existence of a variety of types of iron particles in the atmosphere. Such information can be useful in improving models of iron particles, including deposition and fate in seawater.

Aerosol Health Effects from Molecular to Global Scales

Poor air quality is globally the largest environmental health risk. Epidemiological studies have uncovered clear relationships of gaseous pollutants and particulate matter (PM) with adverse health outcomes, including mortality by cardiovascular and respiratory diseases. Studies of health impacts by aerosols are highly multidisciplinary with a broad range of scales in space and time. We assess recent advances and future challenges regarding aerosol effects on health from molecular to global scales through epidemiological studies, field measurements, health-related properties of PM, and multiphase interactions of oxidants and PM upon respiratory deposition. Global modeling combined with epidemiological exposure-response functions indicates that ambient air pollution causes more than four million premature deaths per year. Epidemiological studies usually refer to PM mass concentrations, but some health effects may relate to specific constituents such as bioaerosols, polycyclic aromatic compounds, and transition metals. Various analytical techniques and cellular and molecular assays are applied to assess the redox activity of PM and the formation of reactive oxygen species. Multiphase chemical interactions of lung antioxidants with atmospheric pollutants are crucial to the mechanistic and molecular understanding of oxidative stress upon respiratory deposition. The role of distinct PM components in health impacts and mortality needs to be clarified by integrated research on various spatiotemporal scales for better evaluation and mitigation of aerosol effects on public health in the Anthropocene.

Predicting ambient aerosol thermal–optical reflectance measurements from infrared spectra: elemental carbon

Elemental carbon (EC) is an important constituent of atmospheric particulate matter because it absorbs solar radiation influencing climate and visibility and it adversely affects human health. The EC measured by thermal methods such as thermal-optical reflectance (TOR) is operationally defined as the carbon that volatilizes from quartz filter samples at elevated temperatures in the presence of oxygen. Here, methods are presented to accurately predict TOR EC using Fourier transform infrared (FT-IR) absorbance spectra from atmospheric particulate matter collected on polytetrafluoroethylene (PTFE or Teflon) filters. This method is similar to the procedure developed for OC in prior work (Dillner and Takahama, 2015). Transmittance FT-IR analysis is rapid, inexpensive and nondestructive to the PTFE filter samples which are routinely collected for mass and elemental analysis in monitoring networks. FT-IR absorbance spectra are obtained from 794 filter samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites collected during 2011. Partial least squares regression is used to calibrate sample FT-IR absorbance spectra to collocated TOR EC measurements. The FT-IR spectra are divided into calibration and test sets. Two calibrations are developed: one developed from uniform distribution of samples across the EC mass range (Uniform EC) and one developed from a uniform distribution of Low EC mass samples (EC < 2.4 mu g, Low Uniform EC). A hybrid approach which applies the Low EC calibration to Low EC samples and the Uniform EC calibration to all other samples is used to produce predictions for Low EC samples that have mean error on par with parallel TOR EC samples in the same mass range and an estimate of the minimum detection limit (MDL) that is on par with TOR EC MDL. For all samples, this hybrid approach leads to precise and accurate TOR EC predictions by FT-IR as indicated by high coefficient of determination (R-2; 0.96), no bias (0.00 mu gm(-3), a concentration value based on the nominal IMPROVE sample volume of 32.8m(3)), low error (0.03 mu g m(-3)) and reasonable normalized error (21 %). These performance metrics can be achieved with various degrees of spectral pretreatment (e.g., including or excluding substrate contributions to the absorbances) and are comparable in precision and accuracy to collocated TOR measurements. Only the normalized error is higher for the FT-IR EC measurements than for collocated TOR. FT-IR spectra are also divided into calibration and test sets by the ratios OC/EC and ammonium/EC to determine the impact of OC and ammonium on EC prediction. We conclude that FT-IR analysis with partial least squares regression is a robust method for accurately predicting TOR EC in IMPROVE network samples, providing complementary information to TOR OC predictions (Dillner and Takahama, 2015) and the organic functional group composition and organic matter estimated previously from the same set of sample spectra (Ruthenburg et al., 2014).

Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid-infrared spectroscopy

In developing partial least squares calibration models, selecting the number of latent variables used for their construction to minimize both model bias and model variance remains a challenge. Several metrics exist for incorporating these trade‐offs, but the cost of model parsimony and the potential for underfitting on achievable prediction errors are difficult to anticipate. We propose a metric that penalizes growing model variance against decreasing bias as additional latent variables are added. The magnitude of the penalty is scaled by a user‐defined parameter that is formulated to provide a constraint on the fractional increase in root mean square error of cross‐validation (RMSECV) when selecting a parsimonious model over the conventional minimum RMSECV solution. We evaluate this approach for quantification of four organic functional groups using 238 laboratory standards and 750 complex atmospheric organic aerosol mixtures with mid‐infrared spectroscopy. Parametric variation of this penalty demonstrates that increase in prediction errors due to underfitting is bounded by the magnitude of the penalty for samples similar to laboratory standards used for model training and validation. Imposing an ensemble of penalties corresponding to a 0–30% allowable increase in RMSECV through sum of ranking differences leads to the selection of a model that increases the actual RMSECV up to 20% for laboratory standards but achieves an 85% reduction in the mean error in predicted concentrations for environmental mixtures. Partial least squares models developed with laboratory mixtures can provide useful predictions in complex environmental samples, but may benefit from protection against overfitting.