Collection and subsequent extraction of particulate matter (PM) from filter substrates is a common requirement for invivo and invitro toxicological studies, as well as chemical analyses such as ion chromatography and inductively coupled plasma mass spectrometry. Several filter extraction protocols exist and different laboratories employ different methods, potentially biasing inter-study comparisons. Previous studies have shown significant differences in extraction efficiency between techniques and identified the relevant extraction artifacts. However, a comprehensive inter-comparison of different methods based on the chemical composition of the extracted PM has never been conducted. In the current study, an exhaustive suite of chemical analyses is performed on PM extracted from glass micro-fiber filters using techniques commonly employed in different laboratories: Multi-solvent extraction (MSE) and spin-down extraction (SDE). PM samples were collected simultaneously during field studies conducted in an urban and rural setting using a high-volume PM2.5 sampler. Results show remarkable compositional variance between the PM extracts for all chemical components analyzed, including metals, water soluble ions, polycyclic aromatic hydrocarbons, non-aromatic organics, elemental carbon and organic carbon. Mass closure was greater than 90% for MSE but deviated substantially for SDE. Detailed retrospective gravimetric analysis of archived SDE samples revealed that a process-based loss of PM mass is the root cause of the differences. These losses are shown to be compositionally biased, both externally between different PM mixtures and internally within a given PM mixture. In combination, the results of this study are the first to demonstrate (i) an exhaustive chemical characterization of a single PM extract, (ii) the significance of directly characterizing the extracted PM used in toxicological studies, (iii) the existence of substantial compositional biases between different filter extraction techniques and (iv) the importance of standardizing filter extraction objectives and procedures to avoid introducing study bias into toxicological studies.

Atmospheric particles are frequently collected onto filter and impactor substrates for studies related to the composition, health effects and climate impact of ambient particulate matter (PM). Many of these studies require extraction of that PM from the substrates but available methods have low extraction efficiencies that may lead to compositional and thus toxicity bias. Here, novel PM extraction methods are presented that (a) maximize extraction efficiency, (b) minimize compositional biases in extracted PM, relative to sampled PM and (c) minimize extraction artifacts. Method development was based upon strengths and weaknesses of existing SOPs and current requirements in the field of aerosol health effects research. Extraction objectives were accomplished using a combination of sonication in solvents of varying polarity, selective filtration, liquid-liquid extraction of water-based extracts, solvent removal and final reconstitution of the total extracted PM. Relying largely on intensive gravimetric analyses and comparison to existing SOPs, the new technique has been fully validated on nearly 40 different size-segregated, source-oriented samples collected during two separate seasons in Fresno, CA. Compared to existing methods, and depending on the source, compositionally-specific increases in extraction efficiencies of 10-40% and 20-50% were obtained for the ultrafine and submicron fine PM fractions, respectively, indicating significant increases in total extraction efficiency and significant decreases in compositional bias. © 2014 Elsevier Ltd.

Previous work successfully implemented a novel system that uses a single particle mass spectrometer to conditionally sample size-segregated, source-oriented particles from the ambient atmosphere in real-time. The underlying hypothesis is that the composition of individual particles is a metric of particle source and thus sampling particles based on composition should be synonymous with sampling based on source. System operation relies on real-time pattern recognition to control the actuation of different ChemVol samplers, where each ChemVol is associated with a unique composition signature. In the current work, a synthesis of data collected during these studies is used in retrospect to reconcile the actual source combinations contributing to the particles collected by each ChemVol. Source attribution is based on correlations between ChemVol sampling periods and coincident wind direction and temporal emissions patterns, coupled to knowledge of single particle composition and surrounding sources. Residential and commercial cooking, vehicular emissions, residential heating and highly processed regional background PM were identified as the major sources. Results show that real-time patterns in single particle mixing state correctly identified specific sources and that these sources were successfully separated into different ChemVols for both summer and winter seasons.

Recent epidemiological evidence supports the hypothesis that health effects from inhalation of ambient particulate matter (PM) are governed by more than just the mass of PM inhaled. Both specific chemical components and sources have been identified as important contributors to mortality and hospital admissions, even when these end points are unrelated to PM mass. Sources may cause adverse health effects via their ability to produce reactive oxygen species in the body, possibly due to the transition metal content of the PM. Our goal is to quantify the oxidative potential of ambient particle sources collected during two seasons in Fresno, CA, using the dithiothreitol (DTT) assay. We collected PM from different sources or source combinations into different ChemVol (CV) samplers in real time using a novel source-oriented sampling technique based on single-particle mass spectrometry. We segregated the particles from each source-oriented mixture into two size fractions - ultrafine (Dp ≤ 0.17 μm) and submicron fine (0.17 μm ≤ Dp ≤ 1.0 μm) - and measured metals and the rate of DTT loss in each PM extract. We find that the mass-normalized oxidative potential of different sources varies by up to a factor of 8 and that submicron fine PM typically has a larger mass-normalized oxidative potential than ultrafine PM from the same source. Vehicular emissions, regional source mix, commute hours, daytime mixed layer, and nighttime inversion sources exhibit the highest mass-normalized oxidative potential. When we apportion DTT activity for total PM sampled to specific chemical compounds, soluble copper accounts for roughly 50% of total air-volume-normalized oxidative potential, soluble manganese accounts for 20%, and other unknown species, likely including quinones and other organics, account for 30%. During nighttime, soluble copper and manganese largely explain the oxidative potential of PM, while daytime has a larger contribution from unknown (likely organic) species.

Abstract. Recent epidemiological evidence supports the hypothesis that health effects from inhalation of ambient particulate matter (PM) are governed by more than just the mass of PM inhaled. Both specific chemical components and sources have been identified as important contributors to mortality and hospital admissions, even when these endpoints are unrelated to PM mass. Sources may cause adverse health effects via their ability to produce reactive oxygen species, possibly due to the transition metal content of the PM. Our goal is to quantify the oxidative potential of ambient particle sources collected during two seasons in Fresno, CA using the dithiothreitol (DTT) assay. We collected PM from different sources or source combinations into different ChemVol (CV) samplers in real time using a novel source-oriented sampling technique based on single particle mass spectrometry. We segregated the particles from each source-oriented mixture into two size fractions – ultrafine (Dp ≤ 0.17 μm) and submicron fine (0.17 μm ≤ Dp ≤ 1.0 μm) – and measured metals and the rate of DTT loss in each PM extract. We find that the mass-normalized oxidative potential of different sources varies by up to a actor of 8 and that submicron fine PM typically has a larger mass-normalized oxidative potential than ultrafine PM from the same source. Vehicular Emissions, Regional Source Mix, Commute Hours, Daytime Mixed Layer and Nighttime Inversion sources exhibit the highest mass-normalized oxidative potential. When we apportion the volume-normalized oxidative potential, which also accounts for the source's prevalence, cooking sources account for 18–29% of the total DTT loss while mobile (traffic) sources account for 16–28%. When we apportion DTT activity for total PM sampled to specific chemical compounds, soluble copper accounts for roughly 50% of total air-volume-normalized oxidative potential, soluble manganese accounts for 20%, and other unknown species, likely including quinones and other organics, account for 30%. During nighttime, soluble copper and manganese largely explain the oxidative potential of PM, while daytime has a larger contribution from unknown (likely organic) species.

Particulate matter (PM) in Imperial Valley originates from a variety of sources such as agriculture, traffic at the border crossing, emissions from the cross-border city of Mexicali, and the drying lakebed of the Salton Sea. Dust storms in Imperial Valley, California regularly lead to exceedances of the federal air quality standards for PM₁₀ (diameter less than 10 microns). To determine if there are differences in the composition and biological response to Imperial County PM by size, ambient PM samples were collected from a sampling unit stationed in the northern-most part of the valley, South of the Salton Sea. Ultrafine, fine, and coarse PM samples were collected and extracted separately. Chemical composition of each size fraction was obtained after extraction by using several analytical techniques, and biological response was measured by exposing a cell line of macrophages to particles and quantifying subsequent gene expression. Biological measurements demonstrated coarse PM induced an inflammatory response in macrophages measured in increases of inflammatory markers IL-1β, IL-6, IL-8 and CXCL2 expression, whereas ultrafine and fine PM only demonstrated significant increases in expression of CYP1a1. These differential responses were due not only to particle size, but to the distinct chemical profiles of each size faction as well. Community groups in Imperial Valley have already completed several projects to learn more about local air quality, giving residents access to data that provides real-time levels of PM_2.5 and PM₁₀ as well as recommendations on health-based practices dependent on the current AQI (air quality index). However, to date there is no information on the composition or toxicity of ambient PM from the region. The data presented here could provide more definitive information on the toxicity of PM by size, and further inform the community on local air quality.