Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data

Exposure to bioaerosols in indoor environments, especially public venues that have a high occupancy and poor ventilation, is a serious public health concern. However, it remains challenging to monitor and determine real-time or predict near-future concentrations of airborne biological matter. In this study, we developed artificial intelligence (AI) models using physical and chemical data from indoor air quality sensors and physical data from ultraviolet light-induced fluorescence observations of bioaerosols. This enabled us to effectively estimate the bioaerosol (bacteria-, fungi- and pollen-like particle) and 2.5-µm and 10-µm particulate matter (PM_2.5 and PM₁₀) on a real-time and near-future (≤60 min) basis. Seven AI models were developed and evaluated using measured data from an occupied commercial office and a shopping mall. A long short-term memory model required a relatively short training time and gave the highest prediction accuracy of ∼ 60 %-80 % for bioaerosols and ∼ 90 % for PM on the testing and time series datasets from the two venues. This work demonstrates how AI-based methods can leverage bioaerosol monitoring into predictive scenarios that building operators can use for improving indoor environmental quality in near real-time.

Bioaerosols in the atmosphere at two sites in Northern Europe in spring 2021: Outline of an experimental campaign

A coordinated observational and modelling campaign targeting biogenic aerosols in the air was performed during spring 2021 at two locations in Northern Europe: Helsinki (Finland) and Siauliai (Lithuania), approximately 500 km from each other in north-south direction. The campaign started on March 1, 2021 in Siauliai (12 March in Helsinki) and continued till mid-May in Siauliai (end of May in Helsinki), thus recording the transition of the atmospheric biogenic aerosols profile from winter to summer. The observations included a variety of samplers working on different principles. The core of the program was based on 2- and 2.4--hourly sampling in Helsinki and Siauliai, respectively, with sticky slides (Hirst 24-h trap in Helsinki, Rapid-E slides in Siauliai). The slides were subsequently processed extracting the DNA from the collected aerosols, which was further sequenced using the 3-rd generation sequencing technology. The core sampling was accompanied with daily and daytime sampling using standard filter collectors. The hourly aerosol concentrations at the Helsinki monitoring site were obtained with a Poleno flow cytometer, which could recognize some of the aerosol types. The sampling campaign was supported by numerical modelling. For every sample, SILAM model was applied to calculate its footprint and to predict anthropogenic and natural aerosol concentrations, at both observation sites. The first results confirmed the feasibility of the DNA collection by the applied techniques: all but one delivered sufficient amount of DNA for the following analysis, in over 40% of the cases sufficient for direct DNA sequencing without the PCR step. A substantial variability of the DNA yield has been noticed, generally not following the diurnal variations of the total-aerosol concentrations, which themselves showed variability not related to daytime. An expected upward trend of the biological material amount towards summer was observed but the day-to-day variability was large. The campaign DNA analysis produced the first high-resolution dataset of bioaerosol composition in the North-European spring. It also highlighted the deficiency of generic DNA databases in applications to atmospheric biota: about 40% of samples were not identified with standard bioinformatic methods.

Challenges and Perspectives for Biosensing of Bioaerosol Containing Pathogenic Microorganisms

As an important route for disease transmission, bioaerosols have received increasing attention. In the past decades, many efforts were made to facilitate the development of bioaerosol monitoring; however, there are still some important challenges in bioaerosol collection and detection. Thus, recent advances in bioaerosol collection (such as sedimentation, filtration, centrifugation, impaction, impingement, and microfluidics) and detection methods (such as culture, molecular biological assay, and immunological assay) were summarized in this review. Besides, the important challenges and perspectives for bioaerosol biosensing were also discussed.

Benchmarking the MinION: Evaluating long reads for microbial profiling

Nanopore based DNA-sequencing delivers long reads, thereby simplifying the decipherment of bacterial communities. Since its commercial appearance, this technology has been assigned several attributes, such as its error proneness, comparatively low cost, ease-of-use, and, most notably, aforementioned long reads. The technology as a whole is under continued development. As such, benchmarks are required to conceive, test and improve analysis protocols, including those related to the understanding of the composition of microbial communities. Here we present a dataset composed of twelve different prokaryotic species split into four samples differing by nucleic acid quantification technique to assess the specificity and sensitivity of the MinION nanopore sequencer in a blind study design. Taxonomic classification was performed by standard taxonomic sequence classification tools, namely Kraken, Kraken2 and Centrifuge directly on reads. This allowed taxonomic assignments of up to 99.27% on genus level and 92.78% on species level, enabling true-positive classification of strains down to 25,000 genomes per sample. Full genomic coverage is achieved for strains abundant as low as 250,000 genomes per sample under our experimental settings. In summary, we present an evaluation of nanopore sequence processing analysis with respect to microbial community composition. It provides an open protocol and the data may serve as basis for the development and benchmarking of future data processing pipelines.