Bioaerosol Detection Research Articles

Bacterial Bioaerosol-Specific Capture and In Situ Detection Using an Immune ZIF-8-Melamine Foam-Functionalized Colorimetric Biosensor.

Bioaerosol infections containing pathogenic viruses and bacteria have resulted in significant economic losses and posed a serious threat to public health, as evidenced by outbreaks of coronavirus disease and avian influenza. Consequently, the sampling and screening of bioaerosols are crucial for the prevention of bioaerosol-borne diseases. In this study, an ultrasensitive biosensor based on zeolitic imidazolate framework-8-melamine foam (ZIF-8-MF) was innovatively developed for the specific capture and in situ detection of bioaerosols. The bacterial bioaerosols were collected by a wet cyclone into phosphate-buffered saline (PBS) buffer at a high collection rate, achieving a satisfactory collection efficiency of ∼80% within 10 min. The target bacteria collected in the PBS buffer were specifically captured and effectively concentrated using immune ZIF-8-MF. The gold@platinum nanozymes (GPNs) were employed to specifically label the captured target bacteria and efficiently amplify the biological signal. And the resulting colorimetric signal was analyzed via a self-developed smartphone application (App). This biosensor demonstrated the capability to detect bioaerosols containing Salmonella typhimurium in the range of 1.6 × 102-1.6 × 105 CFU/m3 within 1.5 h, with a detection limit as low as 100 CFU/m3. Compared with other bioaerosol detection methods, the biosensor offered advantages such as high collection rate, specific capture, efficient concentration, and in situ detection, positioning it as a highly promising and practical tool for the monitoring of bioaerosols containing diverse pathogenic bacteria.

Possible detection of atmospheric bioaerosol via LiDAR: a wavelength-based simulation study

This study explores potential of LiDAR technology to rapidly detect aerosolized biological terror agents in the atmosphere. It assesses the application by simulating extinction coefficients and the Ångström exponent at various wavelengths (266, 1064, 1571, and 2000 nm), focusing on differentiating bioaerosols from typical atmospheric particles. The simulation analysis evaluates changes in aerosol distributions and related extinction coefficient and Ångström exponent shifts under clean, normal, and bad atmospheric conditions. The findings indicate that the 1064 nm wavelength effectively detects bioaerosol presence, with a combination of 1064 nm and 1571 nm providing optimal Ångström exponent use for particle size differentiation. This dual-wavelength approach is highlighted as a practical method for bioaerosol detection, showcasing a significant sensitivity to variations in particle quantity and size, which are critical in biological threat scenarios. In conclusion, the study offers guidance for selecting LiDAR wavelengths for biological agent detection systems. While providing a theoretical framework for practical applications, it also underlines the need for further experimental work to confirm findings and fine-tune technology for real-world monitoring and threat management. This research contributes to the development of effective monitoring strategies against the backdrop of biological terror threats.Graphical

Improved Classification Performance of Bacteria in Interference Using Raman and Fourier-Transform Infrared Spectroscopy Combined with Machine Learning.

The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference.

Spectroscopic detection of bioaerosols with the wibs-4+: Anthropogenic and meteorological impacts

This research builds upon a previous study that explored the potential of the modified WIBS-4+ to selectively differentiate and detect different bioaerosol classes. The current work evaluates the influence of meteorological and air quality parameters on bioaerosol concentrations, specifically pollen and fungal spore dynamics.Temperature was found to be the most influential parameter in terms of pollen production and release, showing a strong positive correlation. Wind data analysis provided insights into the potential geographic origins of pollen and fungal spore concentrations. Fungal spores were primarily shown to originate from a westerly direction, corresponding to agricultural land use, whereas pollen largely originated from a North-easterly direction, corresponding to several forests.The influence of air quality was also analysed to understand its potential impact on the WIBS fluorescent parameters investigated. Most parameters had a negative association with fungal spore concentrations, whereas several anthropogenic influences showed notable positive correlations with daily pollen concentrations. This is attributed to similar driving forces (meteorological parameters) and geographical origins. In addition, the WIBS showed a significant correlation with anthropogenic pollutants originating from combustion sources, suggesting the potential for such modified spectroscopic instruments to be utilized as air quality monitors.By combining all meteorological and pollution data along with WIBS-4+ channel data, a set of Multiple Linear Regression (MLR) analyses were completed. Successful results with R2 values ranging from 0.6 to 0.8 were recorded. The inclusion of meteorological parameters was dependent on the spore or pollen type being examined.

Versatile filter membrane for effective sampling and real-time quantitative detection of airborne pathogens

Construction of air filter membranes bearing prominent collecting and transferring capability is highly desirable for detecting airborne pathogens but remains challenging. Here, a hyaluronic acid air filter membrane (HAFM) with tunable heterogeneous micro-nano porous structures is straightforwardly constructed through the ethanol-induced phase separation strategy. Airborne pathogens can be trapped and collected by HAFM with high performance due to the ideal trade-off between removal efficiency and pressure drop. By exempting the sample elution and extraction processes, the HAFM after filtration sampling can not only directly disperse on the agar plate for colony culture but also turn to an aqueous solution for centrifugal enrichment, which significantly reduces the damage and losses of the captured microorganisms. The following combination with ATP bioluminescence endows the HAFM with a real-time quantitative detection function for the captured airborne pathogens. Benefiting from high-efficiency sampling and non-traumatic transfer of airborne pathogens, the real-world bioaerosol concentration can be facilely evaluated by the HAFM-based ATP assay. This work thus not only provides a feasible strategy to fabricate air filter membranes for efficient microbial collection and enrichment but also sheds light on designing advanced protocols for real-time detection of bioaerosols in the field.

Online monitoring system for qualitative and quantitative analysis of bioaerosols by combined ATP bioluminescence assay with loop-mediated isothermal amplification

Rapid detection of airborne pathogens is crucial in preventing respiratory infections and allergies. However, technologies aiming to real-time analysis of microorganisms in air remain limited due to the sparse and complex nature of bioaerosols. Here, we introduced an online bioaerosol monitoring system (OBMS) comprised of integrated units including a rotatable stainless-steel sintered filter-based sampler, a lysis unit for extracting adenosine triphosphate (ATP), and a single photon detector-based fluorescence unit. Through optimization of the ATP bioluminescence method and establishment of standard curves between relative luminescence units (RLUs) and ATP as well as microbial concentration, we achieved simultaneous detection of bioaerosols' concentration and activity. Testing OBMS with four bacterial and two fungal aerosols at a sampling flow rate of 10 to 50 L/min revealed an outstanding collection efficiency of 95 % at 30 L/min. A single OBMS measurement takes only 8 min (sampling: 5 min; lysis and detection: 3 min) with detection limits of 3 Pcs/ms photons (2.9 × 103 and 292 CFU/m3 for Staphylococcus aureus and Candida albicans aerosol). In both laboratory and field tests, OBMS detected higher concentrations of bioaerosol compared to the traditional Andersen impactor and liquid biosampler. When combined OBMS with loop-mediated isothermal amplification (LAMP), the bioaerosol can be qualitative and quantitative analyzed within 40 min without the cumbersome procedures of sample pretreatment and DNA extraction. These results offer a high compressive and humidity resistance membrane filtration sampler and validate the potential of OBMS for online measurement of bioaerosol concentration and composition.

Gel-confined fabrication of fully bio-based filtration membrane for green capture and rapid detection of airborne microbes

Air filtration has become a desirable route for collecting airborne microbes. However, the potential biotoxicity and sterilization of current air filtration membranes often lead to undesired inactivation of captured microbes, which greatly limits microbial non-traumatic transfer and recovery. Herein, we report a gel-confined phase separation strategy to rationally fabricate a fully bio-based filtration membrane (SGFM) using soluble soybean polysaccharide and gelatin. The versatile SGFM features fascinating honeycomb micro-nano architecture and hierarchical interconnected porous structures for microbial capture, and achieves a lower pressure drop, higher interception efficiency (99.3%), and superior microbial survivability than commercial gelatin filtration membranes. Particularly, the water-dissolvable SGFM can greatly simplify the elution and extraction process after bioaerosol sampling, thereby bringing about maximum sample transfer and vigorous recovery of collected microbes. Meanwhile, green capture coupled with ATP bioluminescence endows the SGFM with rapid and quantitative detection capability for airborne microbes. This work may pave the way for designing green protocols for the detection of bioaerosols.

Research advances in microfluidic collection and detection of virus, bacterial, and fungal bioaerosols.

Bioaerosols are airborne suspensions of fine solid or liquid particles containing biological substances such as viruses, bacteria, cellular debris, fungal spores, mycelium, and byproducts of microbial metabolism. The global Coronavirus disease 2019 (COVID-19) pandemic and the previous emergence of severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and influenza have increased the need for reliable and effective monitoring tools for bioaerosols. Bioaerosol collection and detection have aroused considerable attention. Current bioaerosol sampling and detection techniques suffer from long response time, low sensitivity, and high costs, and these drawbacks have forced the development of novel monitoring strategies. Microfluidic technique is considered a breakthrough for high performance analysis of bioaerosols. In recent years, several emerging methods based on microfluidics have been developed and reported for collection and detection of bioaerosols. The unique advantages of microfluidic technique have enabled the integration of bioaerosol collection and detection, which has a higher efficiency over conventional methods. This review focused on the research progress of bioaerosol collection and detection methods based on microfluidic techniques, with special attention on virus aerosols and bacterial aerosols. Different from the existing reviews, this work took a unique perspective of the targets to be collected and detected in bioaerosols, which would provide a direct index of bioaerosol categories readers may be interested in. We also discussed integrated microfluidic monitoring system for bioaerosols. Additionally, the application of bioaerosol detection in biomedicine was presented. Finally, the current challenges in the field of bioaerosol monitoring are presented and an outlook given of future developments.

Efficient V-Shaped Substrate for Surface and Volume Enhanced Raman Spectroscopic Analysis of Bioaerosol: Prevention from Potential Health Risk.

Bioaerosols pose a great threat to human life and health, and developing highly efficient and accurate identification and analysis methodologies for bioaerosols provides prerequisite knowledge to evaluate their toxicity on human health. In this paper, a rapid and economical method is proposed for the detection of bioaerosols based on surface-enhanced Raman scattering (SERS). The SERS substrate was prepared using an anodic aluminum oxide (AAO) template with an inverted conical (V-shaped) nanopore array structure that was subsequently deposited with Ag nanoparticles (AgNPs) through magnetron sputtering. This tailored nanostructure essentially enables the differentiated and efficient detection of Escherichia coli and Staphylococcus epidermidis health risks within 10 min, along with corresponding detection limits of 103 cells/mL and 104 cells/mL, respectively. Notably, by comparing the Raman spectrum of the two bacteria obtained from the plain plane deposited with the same AgNPs, the V-shaped nanopore array structure provides a more robust enhancement effect in the Raman signal relative to the ordinary plane. More importantly, the applicability of developed AgNPs@V-shaped AAO SERS substrate was validated using ambient bioaerosols, thus emphasizing the great potential of the application of SERS-based technique for a cost-effective, rapid label-free and culture-free bioaerosol analysis without experiencing conventional time-consuming and laborious incubation processes.

New generation sequencing: molecular approaches for the detection and monitoring of bioaerosols in an indoor environment: a systematic review.

The exposure of occupants to indoor air pollutants has increased in recent decades. The aim of this review is to discuss an overview of new approaches that are used to study fungal aerosols. Thus, this motivation was to compensate the gaps caused by the use of only traditional approaches in the study of fungal exposure. The search involved various databases such as; Science Direct, PubMed, SAGE, Springer Link, EBCOHOST, MEDLINE, CINAHL, Cochrane library, Web of Science and Wiley Online Library. It was limited to full text research articles that reported the use of non-viable method in assessing bioaerosol, written in English Language, full text publications and published from year 2015-2022. A total of 15 articles met the inclusion criteria and was included in this review. The use of next-generation sequencing, which is more commonly referred to as high-throughput sequencing (HTS) or molecular methods in microbial studies is based on the detection of genetic material of organisms present in a given sample. Applying these methods to different environments permitted the identification of the microorganisms present, and a better comprehension of the environmental impacts and ecological roles of microbial communities. Based on the reviewed articles, there is evidence that dust samples harbour a high diversity of human-associated bacteria and fungi. Molecular methods such as next generation sequencing are reliable tools for identifying and tracking the bacterial and fungal diversity in dust samples using 18S metagenomics approach.

Rapid bioaerosol detection by measuring circular intensity differential scattering (CIDS) from single flowing through particle

Rapid bioaerosol detection by measuring circular intensity differential scattering (CIDS) from single flowing through particle

A novel strategy for bioaerosol rapid detection based on broad-spectrum high-efficiency magnetic enrichment and separation combined with ATP bioluminescence

Bioaerosol detection technology represented by laser-induced fluorescence (LIF) cannot effectively detect bioaerosols in the presence of interferents such as plant-derived smoke, industrial waste gas, pollen and pollen debris which can produce strong non-biological fluorescence interference. To overcome this drawback, in this study, a novel method based on broad-spectrum high-efficiency magnetic enrichment and separation combined with adenosine triphosphate (ATP) bioluminescence was proposed for Escherichia coli (E. coli) bioaerosols rapid detection. First, E. coli bioaerosols mixed with interferents were collected. Core-shell Fe3O4@Polydopamine@Polyethyleneimine magnetic particles were used as bioaerosol enrichment materials to enrich E. coli bioaerosol sampling solutions. Subsequently, an ATP bioluminescence assay was performed to determine the concentration of E. coli. A linear relationship was observed between ATP bioluminescence intensity after enrichment and the E. coli bioaerosol concentration in the range of 870–49,098 particles per liter; the bioluminescence intensity measured after enrichment was significantly higher than that before enrichment, and this enrichment method provide a 6-fold better sensitivity in bioaerosol detection. More importantly, this method efficiently enriched and detected bioaerosols in plant-derived smoke. This method can effectively improve the sensitivity of ATP bioluminescence detection, and possesses the advantages of convenient operation and strong anti-interference ability. It also provides a foundation for the effective detection of bioaerosols mixed with interfering substances, and a reference for evaluating the sensitivity and anti-interference of LIF-based instruments.

Size-classified monitoring of ATP bioluminescence for rapid assessment of biological distribution in airborne particulates

The COVID-19 pandemic ignited massive research into the rapid detection of bioaerosols. In particular, nanotechnology-based detection strategies are proposed as alternatives because of issues in bioaerosol enrichment and lead time for molecular diagnostics; however, the practical implementation of such techniques is still unclear due to obstacles regarding the large research and development effort and investment for the validation. The use of adenosine triphosphate (ATP) bioluminescence (expressed as relative luminescence unit (RLU) per unit volume of air) of airborne particulate matter (PM) to determine the bacterial population as a representative of the total bioaerosols (viruses, bacteria, and fungi) has been raised frequently because of the high reponse speed, resolution, and compatibility with culture-based bioaerosol monitoring. On the other hand, additional engineering attempts are required to confer significance because of the size-classified (bioluminescence for different PM sizes) and specific (bioluminescence per unit PM mass) biological risks of air for providing proper interventions in the case of airborne transmission. In this study, disc-type impactors to cut-off aerosols larger than 1 μm, 2.5 μm, and 10 μm were designed and constructed to collect PM1, PM2.5, and PM10 on sampling swabs. This engineering enabled reliable size-classified bioluminescence signals using a commercial ATP luminometer after just 5 min of air intake. The simultaneous operations of a six-stage Andersen impactor and optical PM spectrometers were conducted to determine the correlations between the resulting RLU and colony forming unit (CFU; from the Andersen impactor) or PM mass concentration (deriving specific bioluminescence).

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.

Detection of bioaerosols using hyperspectral LIF-LIDAR during S/K challenge II campaign at Dugway

This study showcases the importance of the remote sensing of biological aerosols for both homeland security and environmental protection. We demonstrate the application of a hyperspectral Laser Induced Fluorescence Light Detection and Ranging (LIF-LIDAR) system for the remote sensing of bioaerosols during the international S/K CHALLENGE II field campaign at the U.S. Army Dugway Proving Ground (DPG). The system employs UV photoexcitation at 266 nm and fluorescence spectral analysis in the range of 250–650 nm at a resolution of approximately 2 nm. A two-stage Microchannel Plate (MCP)-ICCD-based receiver capable of single photon-counting detection limit was used. During the campaign, four different bioaerosol simulants were studied in enclosed chambers and open space experiments at night and at distances of 0.5–2 km. A real-time Spectral Angle Mapping (SAM) algorithm was implemented to identify unknown bioaerosol clouds, dust, and combustion plumes as they spread in the open atmosphere. Additionally, we conducted preliminary tests in our institute, which demonstrated the system's ability to identify Ovalbumin (OV) aerosol despite being covered by a thick dust cloud. During the field measurements in DPG, we continuously tracked the fluorescence signature of OV aerosol as it spread in the atmosphere from a distance of 1.5 km for over 20 min. The overall detection performance during the S/K CHALLENGE II was 49 out of 52 trials, indicating a success rate of about 94%. The discussion centers on spectral identification statistics of the unknown dispersed bioaerosol as well as the implications of false-positive and false-negative events.

A Simple Method for Generating Narrowly-dispersed Bioaerosols in Various Sizes

Biological warfare agents (BWAs) cause disease in humans, animals, and plants when purposefully dispersed in an area. To minimize contamination and personnel exposure and initiate early treatment, effective BWA detection or monitoring techniques are needed. Currently, bioaerosol detection or monitoring techniques are used for detecting BWA; however, these techniques have limitations, such as limited sensitivity. To improve the detection performance and develop novel techniques, an additional step in sample preparation, such as obtaining particles of various sizes, is needed. In this study, we investigated the simple and effective generation of bioaerosol particles using a custom-made inkjet aerosol generator (IJAG). Unlike previous inkjet aerosol generators, the operation conditions of the IJAG are fixed at a nozzle heating temperature of 140°C, a driver voltage of 150 V, a pulse width of 60 µs, and a frequency of 250 Hz. The only controlled factor was the concentration of bioaerosol models, including Bacillus globigii spores, ovalbumin, and polystyrene sphere latex. Our system generated bioaerosols with a diameter of 1–8 µm and a narrow distribution size. These results suggest that our IJAG system can achieve the simple and versatile generation of narrow-dispersed bioaerosols for a wide range of available materials. Our study can help improve the sensitivity of detection and monitoring systems for BWAs and bioaerosols.

Advanced CNN Architectures for Pollen Classification: Design and Comprehensive Evaluation

ABSTRACT Allergenic pollen affects the quality of life for over 30% of the European population. Since the treatment efficacy is highly related to the actual exposure to pollen, information about the type and number of airborne pollen grains in real-time is essential for reducing their impact. Therefore, the automation of pollen monitoring has become an important research topic. Our study is focused on the Rapid-E real-time bioaerosol detector. So far, vanilla convolutional neural networks (CNNs) are the only deep architectures evaluated for pollen classification on multi-modal Rapid-E data obtained by exposing collected pollen samples of known classes to the device in a controlled environment. This study contributes to the further development of pollen classification models on Rapid-E data by experimenting with more advanced concepts of CNNs, residual, and inception networks. Our experiments included a comprehensive comparison of different CNN architectures, and obtained results provided valuable insights into which convolutional blocks improve pollen classification. We propose a new model which, coupled with a specific training strategy, improves the current state-of-the-art by reducing its relative error rate by 9%.

Virtual Impactor-Based Label-Free Pollen Detection using Holography and Deep Learning.

Exposure to bio-aerosols such as pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and quantification of various types of pollen. To address this need, we present a mobile and cost-effective label-free sensor that takes holographic images of flowing particulate matter (PM) concentrated by a virtual impactor, which selectively slows down and guides particles larger than 6 μm to fly through an imaging window. The flowing particles are illuminated by a pulsed laser diode, casting their inline holograms on a complementary metal-oxide semiconductor image sensor in a lens-free mobile imaging device. The illumination contains three short pulses with a negligible shift of the flowing particle within one pulse, and triplicate holograms of the same particle are recorded at a single frame before it exits the imaging field-of-view, revealing different perspectives of each particle. The particles within the virtual impactor are localized through a differential detection scheme, and a deep neural network classifies the pollen type in a label-free manner based on the acquired holographic images. We demonstrated the success of this mobile pollen detector with a virtual impactor using different types of pollen (i.e., bermuda, elm, oak, pine, sycamore, and wheat) and achieved a blind classification accuracy of 92.91%. This mobile and cost-effective device weighs ∼700 g and can be used for label-free sensing and quantification of various bio-aerosols over extended periods since it is based on a cartridge-free virtual impactor that does not capture or immobilize PM.

Detection and characterization of bioaerosol emissions from wastewater treatment plants: Challenges and opportunities.

Rapid population growth and urbanization process have led to increasing demand for wastewater treatment capacity resulting in a non-negligible increase of wastewater treatment plants (WWTPs) in several cities around the world. Bioaerosol emissions from WWTPs may pose adverse health risks to the sewage workers and nearby residents, which raises increasing public health concerns. However, there are still significant knowledge gaps on the interplay between process-based bioaerosol characteristics and exposures and the quantification of health risk which limit our ability to design effective risk assessment and management strategies. This review provides a critical overview of the existing knowledge of bioaerosol emissions from WWTPs including their nature, magnitude and size distribution, and highlights the shortcoming associated with existing sampling and analysis methods. The recent advancements made for rapid detection of bioaerosols are then discussed, especially the emerging real time detection methods to highlight the directions for future research needs to advance the knowledge on bioaerosol emissions from WWTPs.

Diagnosis of SARS-CoV-2 infection from breath - a proof-of-concept study

Bioaerosol capture and analysis is emerging as a non-invasive diagnostic method for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this proof-of-concept study conducted in Lesotho, we evaluated the novel and simple AL2 bioaerosol detection device in comparison to conventional nasopharyngeal sampling methods. We demonstrated for the first time that SARS-CoV-2 can be detected using the AL2 bioaerosol capture device. However, studies with a larger sample size are needed to further evaluate this bioaerosol capture device for the detection of SARS-CoV-2.