Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer

Pollen is routinely monitored and forecasted with respect to public health and allergies, but monitoring networks generally utilize a manual process of collection, analysis, and modeling that leads to poor sampling density and high measurement cost. Here, we discuss application of a single-particle fluorescence sensor recently developed for the purpose of real-time detection and recognition of pollen and spores. The sensor operates by collecting fluorescence emission spectra from many individual pollen grains sampled onto a microscope slide for each of four excitation wavelengths (280, 350, 405, and 450 nm) associated with pollen fluorophores. The sensor also records major and minor diameters of each particle. Approximately 25–30 particles for each of eight commercially purchased pollen species were interrogated. Data were analyzed using four classification methods: hierarchical agglomerative and k-means clustering (unsupervised) and random forest and gradient boosting algorithms (supervised). The purpose of the manuscript is to show development of a computational strategy to analyze spectral input data of this kind in order to support further efforts to automate sensor data collection and analysis. Both unsupervised methods showed insufficient accuracy for separating pollen species (76% k-means, 9% HAC) whereas supervised methods performed similarly well (94–95%). The random forest algorithm was then utilized to further optimize operational parameters, based on its higher computational speed. Analyzing the relative importance of each optical source for sensor performance highlighted ways that may be useful to lower sensor cost with minimal reduction to analysis quality. The results provide a framework for the application of this and similar sensors to ambient pollen detection and classification. Copyright © 2020 American Association for Aerosol Research</p

Pollen Classification Using a Single Particle Fluorescence Spectroscopy Technique

Most methods of classifying airborne pollen are labor-intensive and prohibitively expensive, which limits the scales at which predictions about ambient allergy conditions can be provided. A spectroscopic technique was developed towards inexpensively providing information about important pollen groups. The technique uses a single-particle fluorescence spectrometer to analyze super-micron atmospheric bioparticles e.g., pollen and fungal spores, collected onto a substrate. Thirty-four pollen species were sampled from plants at the Denver Botanic Gardens over one year. Particle size and fluorescence emission spectra following excitation from each of four optical sources were collected for each of 916 particles as a first application to freshly collected pollen. Subsequently, a random forest (RF) algorithm was used to classify individual particles, with three key observations. First, categorizing pollen samples into broader categories (sampling month, allergenicity level, or plant type) increased the relative classification accuracy (F value) somewhat, compared to the classification to pollen species. Second, excitation wavelengths most important for classification were identified. Third, comparison of fresh and commercially available pollen of the same species suggests that differences introduced by chemical processing can alter spectroscopic properties and could impact classification. The scope of analysis and particle statistics were limited, because sample collection and spectral analysis were each conducted manually. While particle numbers were too low to extrapolate results toward continuous analysis, results suggest that rough separation of fresh pollen samples is possible using only fluorescence and particle size. The comparison of results may thus be useful to others developing spectroscopic techniques to analyze bioparticles.</p

Impacts of Aerosol Aging on Laser Desorption/Ionization in Single-Particle Mass Spectrometers

<div><p>Single-particle mass spectrometry (SPMS) has been widely used for characterizing the chemical mixing state of ambient aerosol particles. However, processes occurring during particle ablation and ionization can influence the mass spectra produced by these instruments. These effects remain poorly characterized for complex atmospheric particles. During the 2005 Study of Organic Aerosols in Riverside (SOAR), a thermodenuder was used to evaporate the more volatile aerosol species in sequential temperature steps up to 230°C; the residual aerosol particles were sampled by an aerosol mass spectrometer (AMS) and a single-particle aerosol time-of-flight mass spectrometer (ATOFMS). Removal of the secondary species (e.g., ammonium nitrate/sulfate) through heating permitted assessment of the change in ionization patterns as the composition changed for a given particle type. It was observed that a coating of secondary species can reduce the ionization efficiency by changing the degree of laser absorption or particle ablation, which significantly impacted the measured ion peak areas. Nonvolatile aerosol components were used as pseudo-internal standards (or “reference components”) to correct for this LDI effect. Such corrected ATOFMS ion peak areas correlated well with the AMS measurements of the same species up to 142°C. This work demonstrates the potential to accurately relate SPMS peak areas to the mass of specific aerosol components.</p><p>Copyright 2014 American Association for Aerosol Research</p></div

Characterization of Primary Organic Aerosol Emissions from Meat Cooking, Trash Burning, and Motor Vehicles with High-Resolution Aerosol Mass Spectrometry and Comparison with Ambient and Chamber Observations

Organic aerosol (OA) emissions from motor vehicles, meat-cooking and trash burning are analyzed here using a high-resolution aerosol mass spectrometer (AMS). High resolution data show that aerosols emitted by combustion engines and plastic burning are dominated by hydrocarbon-like organic compounds. Meat cooking and especially paper burning emissions contain significant fractions of oxygenated organic compounds; however, their unit-resolution mass spectral signatures are very similar to those from ambient hydrocarbon-like OA, and very different from the mass spectra of ambient secondary or oxygenated OA (OOA). Thus, primary OA from these sources is unlikely to be a significant direct source of ambient OOA. There are significant differences in high-resolution tracer m/zs that may be useful for differentiating some of these sources. Unlike in most ambient spectra, all of these sources have low total m/z 44 and this signal is not dominated by the CO2+ ion. All sources have high m/z 57, which is low during high OOA ambient periods. Spectra from paper burning are similar to some types of biomass burning OA, with elevated m/z 60. Meat cooking aerosols also have slightly elevated m/z 60, whereas motor vehicle emissions have very low signal at this m/z

O/C and OM/OC Ratios of Primary, Secondary, and Ambient Organic Aerosols with High-Resolution Time-of-Flight Aerosol Mass Spectrometry

A recently developed method to rapidly quantify the elemental composition of bulk organic aerosols (OA) using a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) is improved and applied to ambient measurements. Atomic oxygen-to-carbon (O/C) ratios characterize the oxidation state of OA, and O/C from ambient urban OA ranges from 0.2 to 0.8 with a diurnal cycle that decreases with primary emissions and increases because of photochemical processing and secondary OA (SOA) production. Regional O/C approaches ∼0.9. The hydrogen-to-carbon (H/C, 1.4–1.9) urban diurnal profile increases with primary OA (POA) as does the nitrogen-to-carbon (N/C, ∼0.02). Ambient organic-mass-to-organic-carbon ratios (OM/OC) are directly quantified and correlate well with O/C (R2 = 0.997) for ambient OA because of low N/C. Ambient O/C and OM/OC have values consistent with those recently reported from other techniques. Positive matrix factorization applied to ambient OA identifies factors with distinct O/C and OM/OC trends. The highest O/C and OM/OC (1.0 and 2.5, respectively) are observed for aged ambient oxygenated OA, significantly exceeding values for traditional chamber SOA, while laboratory-produced primary biomass burning OA (BBOA) is similar to ambient BBOA, O/C of 0.3–0.4. Hydrocarbon-like OA (HOA), a surrogate for urban combustion POA, has the lowest O/C (0.06–0.10), similar to vehicle exhaust. An approximation for predicting O/C from unit mass resolution data is also presented