Chemical Properties and Single Particle Mixing State of Soot Aerosol in Houston during the TRACER Campaign

Abstract

Abstract. A high-resolution soot particle aerosol mass spectrometer (SP-AMS) was used to selectively measure refractory black carbon (rBC) and its associated coating material using both the ensemble size-resolved mass spectral mode and the event trigger single particle (ETSP) mode in Houston, Texas in summer 2022. This study was conducted as part of the Department of Energy Atmospheric Radiation Measurement (ARM) program’s Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign. The study revealed an average (± 1s) rBC concentration of 103 ± 176 ng m-3. Additionally, the coatings on the BC particles were primarily composed of organics (59 %; 219 ± 260 ng m-3) and sulfate (26 %; 94 ± 55 ng m-3). Positive matrix factorization (PMF) analysis of the ensemble mass spectra of BC-containing particles resolved four distinct types of soot aerosol, including an oxidized organic aerosol (OOABC,PMF) factor associated with processed primary organic aerosol, an inorganic sulfate factor (SO4,BC,PMF), an oxidized rBC factor (O-BCPMF), and a mixed mineral dust/biomass burning aerosol factor with significant contribution from potassium (K-BBBC,PMF). Additionally, K-Means clustering analysis of the single particle mass spectra identified eight different clusters, including soot particles enriched in hydrocarbon like organic aerosol (HOABC,ETSP), sulfate (SO4,BC,ETSP), two types of rBC, OOA (OOABC,ETSP), chloride (ClBC,ETSP) and nitrate (NO3,BC,ETSP). The single particle measurements demonstrate substantial variation in BC coating thickness with coating-to-rBC mass ratios ranging from 0.1 to 100. The mixing state index (χ), which denotes the degree of homogeneity of the soot aerosol, varied from 4 to 94 % with a median of 40 %, indicating that the aerosol population lies in between internal and external mixing but has large temporal and source type variability. In addition, a significant fraction of BC-containing particles, a majority enriched with oxidized organics and sulfate, exhibit sufficiently high κ values and diameters conducive to activation as cloud nuclei under atmospherically relevant supersaturation conditions. This finding bears significance in comprehending the activation of rBC-containing particles as cloud droplets and the origins of CCN in urban areas. Our analysis highlights the complex nature of soot aerosol and underscore the need to comprehend its variability across different environments for accurate assessment of climate change.

Quantifying the Diversity of an Atmospheric Aerosol Population in an Arctic Oil Field on a Single‐Particle Level

“…To date, mixing state index has been applied to various machine learning (Hughes et al, 2018) and global models (Ching et al, 2017(Ching et al, , 2018Shou et al, 2019;Stevens & Dastoor, 2019;Yao et al, 2021Yao et al, , 2022Zheng & Curtis et al, 2021;Zheng & West et al, 2021;Zhu et al, 2016). The metric χ has also been derived using data from laboratory experiments (Dickau et al, 2016), and ambient measurements in the low-latitude regions (Fraund et al, 2017;Singh et al, 2021) and mid-latitude regions, including the contiguous United States and throughout Eurasia (Ching et al, 2019;Farley et al, 2023;Healy et al, 2014;Tomlin et al, 2022;Yu et al, 2020;Zhao et al, 2021). In particular, Healy et al (2014) combined single-particle mass spectrometry data with bulk aerosol mass spectrometry and BC measurements to investigate χ of ambient carbonaceous aerosol in Paris, France.…”

“…For this effort, we used the online mass spectrometry data set previously described by Gunsch et al (2020), who characterized the aerosol chemical composition and contributing sources at Oliktok Point, Alaska during August-September 2016. Previous derivations of the mixing state index χ from ambient aerosol data have utilized single-particle data from either mass spectrometry (Farley et al, 2023;Healy et al, 2014;Lee et al, 2019;Ye et al, 2018;Yu et al, 2020) or microscopy (Bondy et al, 2018;Ching et al, 2019;Fraund et al, 2017;Lata et al, 2021;O'Brien et al, 2015;Singh et al, 2021;Tomlin et al, 2022) methods. In this work, we present a novel approach that integrates both online single-particle mass spectrometry and online bulk aerosol composition data with offline single-particle CCSEM-EDX, for improved characterization of the chemical mixing state of both inorganic-and carbon-rich particles.…”