A flash-lamp based device for fluorescence detection and identification of individual pollen grains

Recent progress in online detection methods of bioaerosols

The aerosol transmission of coronavirus disease in 2019, along with the spread of other respiratory diseases, caused significant loss of life and property; it impressed upon us the importance of real-time bioaerosol detection. The complexity, diversity, and large spatiotemporal variability of bioaerosols and their external/internal mixing with abiotic components pose challenges for effective online bioaerosol monitoring. Traditional methods focus on directly capturing bioaerosols before subsequent time-consuming laboratory analysis such as culture-based methods, preventing the high-resolution time-based characteristics necessary for an online approach. Through a comprehensive literature assessment, this review highlights and discusses the most commonly used real-time bioaerosol monitoring techniques and the associated commercially available monitors. Methods applied in online bioaerosol monitoring, including adenosine triphosphate bioluminescence, laser/light-induced fluorescence spectroscopy, Raman spectroscopy, and bioaerosol mass spectrometry are summarized. The working principles, characteristics, sensitivities, and efficiencies of these real-time detection methods are compared to understand their responses to known particle types and to contrast their differences. Approaches developed to analyze the substantial data sets obtained by these instruments and to overcome the limitations of current real-time bioaerosol monitoring technologies are also introduced. Finally, an outlook is proposed for future instrumentation indicating a need for highly revolutionized bioaerosol detection technologies.

Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications

The article presents the history of the development and the current state of the apparatus for the detection of interferents and biological warfare simulants in the air with the laser-induced fluorescence (LIF) method. The LIF method is the most sensitive spectroscopic method and also enables the measurement of single particles of biological aerosols and their concentration in the air. The overview covers both the on-site measuring instruments and remote methods. The spectral characteristics of the biological agents, steady-state spectra, excitation-emission matrices, and their fluorescence lifetimes are presented. In addition to the literature, we also present our own detection systems for military applications.

Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy

Pollen monitoring have become data-intensive in recent years as real-time detectors are deployed to classify airborne pollen grains. Machine learning models with a focus on deep learning, have an essential role in the pollen classification task. Within this study we developed an explainable framework to unveil a deep learning model for pollen classification. Model works on data coming from single particle detector (Rapid-E) that records for each particle optical fingerprint with scattered light and laser induced fluorescence. Morphological properties of a particle are sensed with the light scattering process, while chemical properties are encoded with fluorescence spectrum and fluorescence lifetime induced by high-resolution laser. By utilizing these three data modalities, scattering, spectrum, and lifetime, deep learning-based models with millions of parameters are learned to distinguish different pollen classes, but a proper understanding of such a black-box model decisions demands additional methods to employ. Our study provides the first results of applied explainable artificial intelligence (xAI) methodology on the pollen classification model. Extracted knowledge on the important features that attribute to the predicting particular pollen classes is further examined from the perspective of domain knowledge and compared to available reference data on pollen sizes, shape, and laboratory spectrofluorometer measurements.

Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations

Pollen is the most common cause of seasonal allergies, affecting over 33 % of the European population, even when considering only grasses. Informing the population and clinicians in real-time about the actual presence of pollen in the atmosphere is essential to reduce its harmful health and economic impact. Thus, there is a growing network of automatic particle analysers, and the reproducibility and transferability of implemented models are recommended since a reference dataset for local pollen of interest needs to be collected for each device to classify pollen, which is complex and time-consuming. Therefore, it would be beneficial to incorporate the reference dataset collected from other devices in different locations. However, it must be considered that laser-induced data are prone to device-specific noise due to laser and detector sensibility. This study collected data from two Rapid-E bioaerosol identifiers in Serbia and Italy and implemented a multi-modal convolutional neural network for pollen classification. We showed that models lost their performance when trained on data from one and tested on another device, not only in terms of the recognition ability but also in comparison with the manual measurements from Hirst-type traps. To enable pollen classification with just one model in both study locations, we first included the missing pollen classes in the dataset from the other study location, but it showed poor results, implying that data of one pollen class from different devices are more different than data of different pollen classes from one device. Combining all available reference data in a single model enabled the classification of a higher number of pollen classes in both study locations. Finally, we implemented a domain adaptation method, which improved the recognition ability and the correlations of transferred models only for several pollen classes.

Real-time automatic detection of starch particles in ambient air

Considerable amounts of starch granules can be present in the atmosphere from both natural and anthropogenic sources. The aim of this study is to investigate the variability and potential origin of starch granules in ambient air recorded at six cities situated in a region with dominantly agricultural land use. This is achieved by using a combination of laser spectroscopy bioaerosol measurements with 1 min temporal resolution, traditional volumetric Hirst type bioaerosol sampling and atmospheric modelling. The analysis of wind roses identified potential sources of airborne starch (i.e., cereal grain storage facilities) in the vicinity of all aerobiological stations analysed in this study. The analysis of the CALPUFF dispersion model confirmed that emission of dust from the location of storage towers situated about 2.5 km north of the aerobiological station in Novi Sad is a plausible source of high airborne concentrations of starch granules. This study is important for environmental health since it contributes body of knowledge about sources, emission, and dispersion of airborne starch, known to be involved in phenomena such as thunderstorm-triggered asthma. The presented approach integrates monitoring and modelling, and provides a roadmap for examining a variety of bioaerosols previously considered to be outside the scope of traditional aerobiological measurements.

Why should we care about high temporal resolution monitoring of bioaerosols in ambient air?

This is the first time that atmospheric concentrations of individual pollen types have been recorded by an automatic sampler with 1-hour and sub-hourly resolution (i.e. 1-minute and 1-second data). The data were collected by traditional Hirst type methods and state-of the art Rapid-E real-time bioaerosol detector. Airborne pollen data from 7 taxa, i.e. Acer negundo, Ambrosia, Broussonetia papyrifera, Cupressales (Taxaceae and Cupressaceae families), Platanus, Salix and Ulmus, were collected during the 2019 pollen season in Novi Sad, Serbia. Pollen data with daily, hourly and sub-hourly temporal resolution were analysed in terms of their temporal variability. The impact of turbulence kinetic energy (TKE) on pollen cloud homogeneity was investigated. Variations in Seasonal Pollen Integrals produced by Hirst and Rapid-E show that scaling factors are required to make data comparable. Daily average and hourly measurements recorded by the Rapid-E and Hirst were highly correlated and so examining Rapid-E measurements with sub-hourly resolution is assumed meaningful from the perspective of identification accuracy. Sub-hourly data provided an insight into the heterogenous nature of pollen in the air, with distinct peaks lasting ~5-10 min, and mostly single pollen grains recorded per second. Short term variations in 1-minute pollen concentrations could not be wholly explained by TKE. The new generation of automatic devices has the potential to increase our understanding of the distribution of bioaerosols in the air, provide insights into biological processes such as pollen release and dispersal mechanisms, and have the potential for us to conduct investigations into dose-response relationships and personal exposure to aeroallergens.

Field Evaluation of an Automated Pollen Sensor

Background: Seasonal pollen is a common cause of allergic respiratory disease. In the United States, pollen monitoring occurs via manual counting, a method which is both labor-intensive and has a considerable time delay. In this paper, we report the field-testing results of a new, automated, real-time pollen imaging sensor in Atlanta, GA. Methods: We first compared the pollen concentrations measured by an automated real-time pollen sensor (APS-300, Pollen Sense LLC) collocated with a Rotorod M40 sampler in 2020 at an allergy clinic in northwest Atlanta. An internal consistency assessment was then conducted with two collocated APS-300 sensors in downtown Atlanta during the 2021 pollen season. We also investigated the spatial heterogeneity of pollen concentrations using the APS-300 measurements. Results: Overall, the daily pollen concentrations reported by the APS-300 and the Rotorod M40 sampler with manual counting were strongly correlated (r = 0.85) during the peak pollen season. The APS-300 reported fewer tree pollen taxa, resulting in a slight underestimation of total pollen counts. Both the APS-300 and Rotorod M40 reported Quercus (Oak) and Pinus (Pine) as dominant pollen taxa during the peak tree pollen season. Pollen concentrations reported by APS-300 in the summer and fall were less accurate. The daily total and speciated pollen concentrations reported by two collocated APS-300 sensors were highly correlated (r = 0.93–0.99). Pollen concentrations showed substantial spatial and temporal heterogeneity in terms of peak levels at three locations in Atlanta. Conclusions: The APS-300 sensor was able to provide internally consistent, real-time pollen concentrations that are strongly correlated with the current gold-standard measurements during the peak pollen season. When compared with manual counting approaches, the fully automated sensor has the significant advantage of being mobile with the ability to provide real-time pollen data. However, the sensor’s weed and grass pollen identification algorithms require further improvement.

High-Resolution Fluorescence Spectra of Airborne Biogenic Secondary Organic Aerosols: Comparisons to Primary Biological Aerosol Particles and Implications for Single-Particle Measurements

Aqueous extracts of biogenic secondary organic aerosols (BSOAs) have been found to exhibit fluorescence that may interfere with the laser/light-induced fluorescence (LIF) detection of primary biological aerosol particles (PBAPs). In this study, we quantified the interference of BSOAs to PBAPs by directly measuring airborne BSOA particles, rather than aqueous extracts. BSOAs were generated by the reaction of d-limonene (LIM) or α-pinene (PIN) and ozone (O₃) with or without ammonia in a chamber under controlled conditions. With an excitation wavelength of 355 nm, BSOAs exhibited peak emissions at 464-475 nm, while fungal spores exhibited peak emissions at 460-483 nm; the fluorescence intensity of BSOAs with diameters of 0.7 μm was in the same order of magnitude as that of fungal spores with diameters of 3 μm. The number fraction of 0.7 μm BSOAs that exhibited fluorescence above the threshold was in the range of 1.9-15.9%, depending on the species of precursors, relative humidity (RH), and ammonia. Similarly, the number fraction of 3 μm fungal spores that exhibited fluorescence above the threshold was 4.9-36.2%, depending on the species of fungal spores. Normalized fluorescence by particle volumes suggests that BSOAs exhibited fluorescence in the same order of magnitude as pollen and 10-100 times higher than that of fungal spores. A comparison with ambient particles suggests that BSOAs caused significant interference to ambient fine particles (15 of 16 ambient fine particle measurements likely detected BSOAs) and the interference was smaller for ambient coarse particles (4 of 16 ambient coarse particle measurements likely detected BSOAs) when using LIF instruments.

Laboratory evaluation of the (VIS, IR) scattering matrix of complex-shaped ragweed pollen particles

Ragweed or Ambrosia artemisiifolia pollen is an important atmospheric constituent affecting the Earth's climate and public health. The literature on light scattering by pollens embedded in ambient air is however rather sparse: polarization measurements are limited to the sole depolarization ratio and pollens are beyond the reach of numerically exact light scattering models mainly due to their tens of micrometre size. Also, ragweed pollen presents a very complex shape, with a small-scale external structure exhibiting spikes that bears some resemblance with coronavirus, but also apertures and micrometre holes. In this paper, to face such a complexity, a controlled-laboratory experiment is proposed to evaluate the scattering matrix of ragweed pollen embedded in ambient air. It is based on a newly-built polarimeter, operating in the infra-red spectral range, to account for the large size of ragweed pollen. Moreover, the ragweed scattering matrix is also evaluated in the visible spectral range to reveal the spectral dependence of the ragweed scattering matrix within experimental error bars. As an output, precise spectral and polarimetric fingerprints for large size and complex-shaped ragweed pollen particles are then provided. We believe our laboratory experiment may interest the light scattering community by complementing other light scattering experiments and proposing outlooks for numerical work on large and complex-shaped particles.

A demonstration project of Global Alliance against Chronic Respiratory Diseases: Prediction of interactions between air pollution and allergen exposure-the Mobile Airways Sentinel NetworK-Impact of air POLLution on Asthma and Rhinitis approach

This review analyzes the state and recent progress in the field of information support for pollen allergy sufferers. For decades, information available for the patients and allergologists consisted of pollen counts, which are vital but insufficient. New technology paves the way to substantial increase in amount and diversity of the data. This paper reviews old and newly suggested methods to predict pollen and air pollutant concentrations in the air and proposes an allergy risk concept, which combines the pollen and pollution information and transforms it into a qualitative risk index. This new index is available in an app (Mobile Airways Sentinel NetworK-air) that was developed in the frame of the European Union grant Impact of Air POLLution on sleep, Asthma and Rhinitis (a project of European Institute of Innovation and Technology-Health). On-going transformation of the pollen allergy information support is based on new technological solutions for pollen and air quality monitoring and predictions. The new information-technology and artificial-intelligence-based solutions help to convert this information into easy-to-use services for both medical practitioners and allergy sufferers.

Intercomparison of Multiple UV-LIF Spectrometers Using the Aerosol Challenge Simulator

Measurements of primary biological aerosol particles (PBAPs) have been conducted worldwide using ultraviolet light-induced fluorescence (UV-LIF) spectrometers. However, how these instruments detect and respond to known biological and non-biological particles, and how they compare, remains uncertain due to limited laboratory intercomparisons. Using the Defence Science and Technology Laboratory, Aerosol Challenge Simulator (ACS), controlled concentrations of biological and non-biological aerosol particles, singly or as mixtures, were produced for testing and intercomparison of multiple versions of the Wideband Integrated Bioaerosol Spectrometer (WIBS) and Multiparameter Bioaerosol Spectrometer (MBS). Although the results suggest some challenges in discriminating biological particle types across different versions of the same UV-LIF instrument, a difference in fluorescence intensity between the non-biological and biological samples could be identified for most instruments. While lower concentrations of fluorescent particles were detected by the MBS, the MBS demonstrates the potential to discriminate between pollen and other biological particles. This study presents the first published technical summary and use of the ACS for instrument intercomparisons. Within this work a clear overview of the data pre-processing is also presented, and documentation of instrument version/model numbers is suggested to assess potential instrument variations between different versions of the same instrument. Further laboratory studies sampling different particle types are suggested before use in quantifying impact on ambient classification.

Development and characterization of an inexpensive single-particle fluorescence spectrometer for bioaerosol monitoring

Laser-induced fluorescence (LIF) techniques to analyze atmospheric aerosols are commonly applied for research and human exposure monitoring, but are very expensive or offer poor spectral resolution. Here, we discuss how a recently proposed instrument can acquire resolved fluorescence spectra from many individual particles in a single camera image using four excitation wavelengths matched with common biological fluorophores for particle discrimination at lower cost. We discuss emission intensity calibration and demonstrate spectral differentiation among four species of pollen. These data provide context for how the instrument could be developed for pollen and mold-spore detection or for use by citizen scientists.

Time-resolved monitoring of polycyclic aromatic hydrocarbons adsorbed on atmospheric particles

Real-time monitoring of individual particles from atmospheric aerosols was performed by means of a specifically developed single-particle fluorescence spectrometer (SPFS). The observed fluorescence was assigned to particles bearing polycyclic aromatic hydrocarbons (PAH). This assignment was supported by an intercomparison with classical speciation on filters followed by gas chromatography-mass spectrometry (GC-MS) analysis. As compared with daily averaged data, our time-resolved approach provided information about the physicochemical dynamics of the particles. In particular, distinctions were made between background emissions related to heating, and traffic peaks during rush hours. Also, the evolution of the peak fluorescence wavelength provided an indication of the aging of the particles during the day.

Assessing the Dynamics of Organic Aerosols over the North Atlantic Ocean

The influence of aerosols on climate is highly dependent on the particle size distribution, concentration, and composition. In particular, the latter influences their ability to act as cloud condensation nuclei, whereby they impact cloud coverage and precipitation. Here, we simultaneously measured the concentration of aerosols from sea spray over the North Atlantic on board the exhaust-free solar-powered vessel “PlanetSolar”, and the sea surface physico-chemical parameters. We identified organic-bearing particles based on individual particle fluorescence spectra. Organic-bearing aerosols display specific spatio-temporal distributions as compared to total aerosols. We propose an empirical parameterization of the organic-bearing particle concentration, with a dependence on water salinity and sea-surface temperature only. We also show that a very rich mixture of organic aerosols is emitted from the sea surface. Such data will certainly contribute to providing further insight into the influence of aerosols on cloud formation, and be used as input for the improved modeling of aerosols and their role in global climate processes.

Discriminating Bio-aerosols from Non-Bio-aerosols in Real-Time by Pump-Probe Spectroscopy

The optical identification of bioaerosols in the atmosphere and its discrimination against combustion related particles is a major issue for real-time, field compatible instruments. In the present paper, we show that by embedding advanced pump-probe depletion spectroscopy schemes in a portable instrument, it is possible to discriminate amino acid containing airborne particles (bacteria, humic particles, etc.) from poly-cyclic aromatic hydrocarbon containing combustion particles (Diesel droplets, soot, vehicle exhausts) with high selectivity. Our real-time, multi-modal device provides, in addition to the pump-probe depletion information, fluorescence spectra (over 32 channels), fluorescence lifetime and Mie scattering patterns of each individually flowing particle in the probed air.

Spectrally-resolved fluorescence cross sections of aerosolized biological live agents and simulants using five excitation wavelengths in a BSL-3 laboratory

A system for measuring spectrally-resolved fluorescence cross sections of single bioaerosol particles has been developed and employed in a biological safety level 3 (BSL-3) facility at Edgewood Chemical and Biological Center (ECBC). It is used to aerosolize the slurry or solution of live agents and surrogates into dried micron-size particles, and to measure the fluorescence spectra and sizes of the particles one at a time. Spectrally-resolved fluorescence cross sections were measured for (1) bacterial spores: Bacillus anthracis Ames (BaA), B. atrophaeus var. globigii (BG) (formerly known as Bacillus globigii), B. thuringiensis israelensis (Bti), B. thuringiensis kurstaki (Btk), B. anthracis Sterne (BaS); (2) vegetative bacteria: Escherichia coli (E. coli), Pantoea agglomerans (Eh) (formerly known as Erwinia herbicola), Yersinia rohdei (Yr), Yersinia pestis CO92 (Yp); and (3) virus preparations: Venezuelan equine encephalitis TC83 (VEE) and the bacteriophage MS2. The excitation wavelengths were 266 nm, 273 nm, 280 nm, 365 nm and 405 nm.