Exposometer-based assessment of laboratory work environment

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Pimenoff, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8058538/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Human health is impacted by a wide range of exposures, including biological, chemical, and physical factors, making it challenging to assess their combined environmental health effects comprehensively. Advances in wearable and stationary sensor devices enable systematic longitudinal measurement of biotic and abiotic exposures. In this pilot study at the Center for Cervical Cancer Elimination (Karolinska Institutet, Sweden), we deployed stationary exposometer devices in three different laboratory locations (the pre-PCR "clean-room", the molecular analysis room, and the sample management/reception room) to assess the indoor biotic and abiotic aerosol exposures. Of all biotic sequences obtained after amplification, 0.56% were classified as known microorganisms. The aerosol profile of the clean room differed markedly from the sample reception and the analysis rooms, which exhibited similar biotic profiles at both the phylum level (all kingdoms) and species level for eukaryotic and viral fractions. The laboratory is an international reference laboratory for Human Papillomavirus, but this virus was not detected in the work environment in any location. Abiotic exposures displayed diurnal dynamics, with higher daytime light and carbon dioxide levels and higher volatile organic compounds (VOC) levels at night. These results demonstrate the feasibility of using exposometers for detailed indoor aerosol exposure assessment in occupational settings. Earth and environmental sciences/Environmental sciences Biological sciences/Microbiology Human exposome biological exposures chemical exposures physical exposures exposometer devices Figures Figure 1 Figure 2 Figure 3 Introduction The exposome includes all biological agents, chemical compounds, and physical factors present in the environment that impact human health 1 , 2 . The environmental exposures may vary even within individuals living in the same geographic area 3 making it challenging to assess all the exposures in the environment that may affect humans. The microbiome (all microorganisms that humans are exposed to) is an intensely studied part of the expsome 4 – 8 . Microorganisms are key modulators of human health, as commensals and health-detrimental factors 5 – 7 , 9 .Environmental microbiome research has mainly focused on soil and water ecosystems, while airborne microbes have been understudied 10 . Direct inhalation of microbes, which can integrate into the respiratory microbiome, could modulate the susceptibility to inhaled irritants 11 while inhaled compounds could also alter respiratory microbiome profiles 12 . On the other hand, abiotic exposures (physical and chemical) have also been extensively studied, especially for radiation, gas concentrations, or particles. Still, fewer studies focused on the exposure to airborne microbes and even fewer on the combination of both biotic and abiotic factors in the environment 3 , 13 – 15 . Work environment exposures constitute a crucial part of the exposome for an average adult; therefore, it is critical to ensure a healthy and safe environment at work. Particularly for laboratories, exposures can be more hazardous as laboratories commonly handle biological samples as well as health-detrimental chemical compounds. Recent developments in exposometer devices have been key to shifting the exposure studies towards a more comprehensive characterization, which allows both the integration of multiple data types 3 , 15 and the development of monitoring systems for highly specialized applications 16 . The exhaustive study of the human exposome will substantially improve the understanding of how the environment shapes our health and will be of special interest in the context of exposure monitoring. This work aims to present a proof-of-concept on biotic and abiotic monitoring of exposures in a work environment. Results Overall taxonomic classification Human DNA removal was performed in two steps: a) using NextGenMap, and b) using kraken2. NextGenMap identified a total of 3.8M human reads (1.24% of all reads) in the clean room, 10.9M human reads (3.60% of all reads) in the analysis room, and a total of 38.24M human reads (12.33% of all reads) in the sample reception. After the first step of human DNA removal, kraken2 identified 27.45M, 55.67M, and 41.83M human reads in the clean room, analysis room, and sample reception. Reads classified as known microorganisms corresponded to 0.53% of all initial reads for the clean room, 0.62% for the analysis room, and 0.53% for the sample reception. Overall, 0.56% of all reads across sample locations were classified as known microorganisms, comprising a total of 5.15M reads representing 10,910 different taxa. Taxa with less than 10 reads were then filtered out in order to remove potential sequencing artifacts, resulting in a total of 5,738 taxa. From these, the vast majority of taxa belonged to bacteria (n=5,205), followed by archaea (n=213), eukaryotes (n=187), and viruses (n=133). Relative abundance among remaining taxa ranged between 0.000195% and 8.13%. The total reads classified as non-human, at any taxonomic level and with more than 10 reads, considered in the downstream analysis, was above 5.13M reads. From those, the analysis room presented 1.88M reads, followed by the sample reception with 1.65M reads and the clean room with 1.60M reads. The location with the most unique taxa was the clean room with 270 unique taxa, followed by the analysis room with 202, and the sample reception with 126. The three locations shared 3,183 taxa, while the clean room shared 541 with the sample reception and 1,013 with the analysis room. The sample reception and the analysis room shared 403 taxa. The number of taxa specific for each group and shared between groups is available in Figure 1A. The overall taxonomic resolution at the species level reached 92.04%, being the highest among bacteria (92.57%), followed by archaea (91.55%), viruses (90.98%), and eukaryotes (78.61%). Species diversity and core microbiome composition Filtering taxa with at least 10 reads, and after rarefaction at 1.38M reads, alpha diversity at the species-level revealed a higher number of total observed species in the clean room, with a total of 4,388 species. The analysis room and the sample reception obtained a total number of observed species of 4,090 and 3,545, respectively. In contrast, the highest Shannon and inverse Simpson indexes corresponded to the sample reception. Even though differences in location were not wide in Shannon index (4.60 in the sample reception, 4.46 in the analysis room, and 4.43 in the clean room), differences in inverse Simpson index were wider with respect to the highest value (37.19 in the sample reception, 17.22 in the clean room, and 16.91 in analysis room) as shown in Table 1. While the Shannon index focuses on richness and evenness, the Inverse Simpson index focuses on evenness and dominance of specific taxa, being less sensitive towards rare taxa than the Shannon index. The fact that the sample reception presents a much higher Inverse Simpson index suggests a more even distribution of dominant species than in the other locations. The abovementioned alpha diversity indices were also calculated after filtering taxa for >1 reads (rarefacted at 1.39M) and ³100 reads (rarefacted at 1.33M reads) to ensure robustness of the results, displaying the same diversity patterns across sample locations (Table 1). When focusing on the main phylum across samples, we observed more similar compositions between the analysis room and the sample reception. In contrast, the phylum composition of the clean room was more differential. The clean room presented lower proportions of Ascomycota (fungi), Bacillota (bacteria), and Basidiomycota (fungi). In contrast, Actinomycetota (bacteria), Apicomplexa (protist), Bacteroidota (bacteria), Campylobacterota (bacteria), and Pseudomonadota (bacteria) presented higher proportions in the clean room in comparison to the other locations. Even though the analysis room and the sample reception presented more similar compositions, some differences were also observed for Ascomycota (fungi) and Euglenozoa (protist), which were more abundant in the analysis room. In contrast, Bacillota (bacteria), Bacteroidota (bacteria), Basidiomycota (fungi), and Euryachaeota (archaea) were higher in the sample reception (Figure 1B). The number of reads classified per phylum and sample location is available in Table 2. Kingdom-specific core species Archaea Most abundant archaeal species were unique across locations (Figure 2A). The sample reception presented a clear dominance of Halobaculum sp. DT55 , followed by Haloterrigena turkmenica and Halomicrobium urmianum, while the most abundant archaeal species in the analysis room were Methanosarcina siciliae followed by Methanosarcina barkeri and Nanobdella aerobiophila . The clean room presented a clear dominance of Candidatus Aciduliprofundum sp. MAR08-339 , followed by Natromonas gomsonensis, Candidatus Prometheoarchaeum syntrophicum , and Methanobrevidbacter olleyae. Bacteria Dominant bacterial species profiles were also divergent across sample locations (Figure 2B). However, some similarities were observed between the sample reception and the analysis room, sharing the presence of Cutibacterium acnes and Streptococcus thermophilus, and between the clean room and the analysis room, sharing Bacillus velezensis . Bacterial species Escherichia coli and Moraxella osloensis were present across all three locations. Location-unique bacterial species were present only in the sample reception ( Caldinitratiruptor microaerophilus ) and in the clean room ( Amycolatopsis sp NBC 01480 , Klebisella pneumoniae , Pseudoaltromonas undina , and Rhodococcus globerulus ). Eukaryota Eukaryotic species composition in the sample reception and the analysis room displayed more closely related profiles compared to the clean room (Figure 2C). However, all the dominant species in this Kingdom were present in all the sample locations. Major compositional differences included the high abundance of Plasmodium coatneyi and Aspergillus luchiensis in the clean room, Zymoseptoria tritici and Fluvia fulva in the analysis room, and Botrytis cinerea and Prycularia oryzar in the sample reception, even though these two last species did not present as relevant differences across locations as the others. Virus The composition of the most abundant viral species among sample locations presented different profiles (Figure 2D). While the analysis room and the sample reception shared the main viral species ( Nanovirus Black medic leaf roll virus ), the clean room presented a predominance of Alphabaculovirus agipsilonis , which was not present in the other locations. Other unique viral species only found in the clean room were Pinus nigra virus 1 , Fowl aviadenovirus C, and Megrivirus Hubei picorna-like virus 24 . The sample reception presented two unique viral species: Pandoravirus neocaledonia and Tupanvirus soda lake . Papillomavirus across sample locations The Papillomaviridae family presented four different genera according to unfiltered Kraken2 results across samples, namely Alphapapillomavirus , Deltapapillomavirus , Gammapapillomavirus and Psipapillomavirus . However, when filtering out taxa with less than 10 reads, only Gammapapillomavirus remained, suggesting that the other genera were probably sequencing artifacts. Further analyses for Human Papillomavirus-specific characterization using DIAMOND 17 revealed that even though all raw samples had reads aligning against HPV, none of the locations presented more than 10 reads with at least a 90% identity. However, the sample reception was the only location presenting reads with ≥90% identity, with three reads above the identity threshold for HPV-mSK152, but only 108bp coverage. We can therefore conclude that none of the three locations presented environmental Papillomavirus. Abiotic exposures Abiotic exposures included PM2.5MC, LUX, CO 2 , VOCRaw, and NOXRaw, monitored for 7 days for the three locations. The majority of exposures displayed rhythmic variations according to workday schedules. Focusing on PM2.5MC, the pattern was exactly the same in the three locations, with a high peak during the night of the second day to the third day, followed by a more discrete peak the next night, and at the end of the study period. Light also followed diurnal patterns, with notable differences between the three locations. Regarding CO 2 and VOCRaw, we also found diurnal patterns that were opposite in this case: higher CO 2 during the day and higher VOCRaw during nights. Finally, NOXRaw was incremental over time with differential patterns depending on the sample location. Discussion Most research on human health exposures has focused on the abiotic fraction (radiation, gas concentrations, particles, among others) or the biotic fraction separately 1 , 2 . However, studies that integrate the characterization of both types of exposures are scarce 3 , 13 – 15 . The present work serves as a proof-of-concept for environmental studies using exposome data, including both biotic and abiotic fractions, from a use case in a laboratory work environment. Results obtained within the study characterized different biotic compositions across sample locations, which were even noticeable at the phylum level. When focusing on the species level, Cutibacterium acnes , a commensal bacteria found on human skin 18 , was found as one of the most abundant species in both the sample reception and the analysis room. In contrast, the clean room presented negligible amounts of this bacterial species as the protocols for decontamination were stricter than in the other rooms. These findings agreed with the percentage of removed human DNA over the total reads, which was the lowest in the clean room. In contrast, the clean room presented a strong presence of Amycolatopsis sp NBC 01480 bacterial species. Other Amycolatopsis species have been previously described as spore-forming microorganisms surviving sterilization in clean rooms at NASA 19 , evidencing the challenge of eliminating these bacteria even in highly restrictive decontamination conditions. Samples used in the study were taken from different locations within the Center for Cervical Cancer Elimination (CCCE), where clinical samples for HPV genotyping screening tests are received, processed, and analyzed. The CCCE holds the National and International Reference Laboratories for HPV. For this reason, we aimed to investigate the presence of the Papillomavirus family among locations using both the main microbiome characterization pipeline and another specific pipeline for Human Papillomavirus (HPV) detection. Even though some Papillomavirus reads were classified with both of the pipelines, none surpassed the cut-offs established to consider its presence in any of the locations, implying absence of potential contamination of PCR-derived HPV DNA in laboratory room aerosols and therefore also the absence of HPV risk exposures in the work environment. The study displayed temporal and spatial differences when focusing on the abiotic fraction. Clear rhythmic daily variations were observed for LUX, CO 2 and VOCRaw. At the same time, both LUX and CO 2 increased during working hours (day) (with some location variations due to different natural light conditions and different flow of workers in each of the rooms), VOCRaw increased after working hours (night) in all locations, with some variations. The main limitation of this study is the number of samples taken. Since the present work aimed to serve as a use case and not a comprehensive comparison between location exposures, only one device was used per location (n = 3). Moreover, the biotic fraction represented a snapshot at the end of the measurement period, and there is no information on how these biotic exposures varied over time, as we have this information only for the abiotic fraction. It is important to note that the exposometers' data were collected during a week in June 2025; thus, the results must be interpreted considering the data's seasonality. Multiple studies have shown variations in environmental microorganisms depending on the season 3 , 20 . Hence, the biotic compositions for the locations described might vary depending on when the measures are taken. Even though the present work serves as a proof-of-concept, as most of the studies conducted using exposometers to date 3 , 14 , 15 , the results presented here confirm the potential of these devices to be implemented in different settings for a variety of use cases such as hospital monitoring (ICU, nosocomial infections, or environmental laboratory safety monitoring), environmental microbiome surveillance (outbreaks, ecosystem changes, seasonal infections), or in public health control (health-detrimental exposures in a specific region or area). The information obtained from the devices could also be integrated with other real-time data sources to capture a broader range of data types, enabling multimodal data integration and offering a more comprehensive overview of exposures. Additionally, advanced analytical techniques such as mass spectrometry could be employed to achieve a more precise molecular characterization of the exposures. The present work is a case study of how stationary exposometer devices could inform about both biotic and abiotic exposures in specific locations, providing a powerful tool for monitoring potential health-detrimental factors in different environments. Methods Exposometer devices Stationary exposometer devices were used to measure both abiotic and biotic exposures in 3 different work environment locations within the Center of Cervical Cancer Elimination at Karolinska Institutet (Stockholm, Sweden) for 7 days (9th to 16th June 2025). The devices were placed in the 1) sample reception, where HPV screening tests are received and registered, 2) in a pre-PCR room named the clean room, and 3) in the analysis room, where the PCR machines used for HPV genotyping are located. The exposometer devices registered information for fine particulate matter mass concentration, CO 2 concentration, VOCs and NOx, light (Lux, IR, UV, UVindex), pressure, relative humidity, sample flow rate, and differential pressure across the sample filter. The biotic fractions were captured from the filters at the end of the measurements. Device product information and user guide from Access Sensor Technologies are available in https://www.accsensors.com/upasv2.1plus . Filters extraction and sequencing Exposometer filters were cut with sterile scissors into smaller pieces and introduced into a tube with BD diluent at room temperature for 1 hour. Afterward, each filter was extracted in 4 tubes of 200µl each, eluted in 50µl and finally pooled into one tube per filter. Extraction was performed using the extraction kit Magtration Reagent MagDEA Dx SV (Precision System Science, PSS) in MagLead and followed by DNA amplification using REPLI-g Single Cell kit (Qiagen) according to the manufacturer's reference guide with 17h of incubation at 30°C. After incubation, the samples were inactivated for 3 minutes at 65°C. Amplified material was thereafter subjected to library preparation using SMARTer Stranded Total RNA-Seq Kit v2 (Takara Bio USA) library preparation kit following the manufacturer's reference guide. The libraries were validated, normalized to 2 nM, and pooled. Pair-end sequencing, 2x150 cycles, was performed using NextSeq550 (Illumina, San Diego, CA). Biotic profiling Bioinformatic preprocessing Sequence reads obtained from NextSeq500 (Illumina) were assigned to the samples using the indexes in the adapters. Quality trimming and adaptor removal were performed using Trimmomatic 21 (v0.39) with a minimal length of 18bp and using the option keepBothReads. Then, human reads were removed using NextGenMap 22 (v0.5.5) using 95% of identity and 75% of the read length against GRCh38 and GCA-009914755.4-T2T-CHM13v2.0 human references. Non-human reads were retrieved, merged by sample, sorted, and converted to fastq files using samtools (v1.6). Microbiome profiling Taxonomic classification of remaining reads was performed using Kraken2 23 (v2.1.2) against all RefSeq curated genomes for archaea, bacteria, viruses, protozoa, and fungi, as well as human, plasmids and univec potential contaminants (database obtained from https://benlangmead.github.io/aws-indexes/k2 , collection PlusPF). Reports generated by kraken2 were transformed to biom files using the kraken2biom tool 24 and then imported to R (v4.2.2). Downstream microbiome analyses were conducted using the following packages: phyloseq 25 , ggplot2 26 , tidytacos 27 , and dplyr 28 . The total number of taxa and reads across all samples was evaluated after removing human and plasmid reads. Taxa with fewer than 10 reads were filtered out to remove potential sequencing artifacts. Taxonomic resolution was assessed at the species level by dividing the total number of taxa classified at the species level by the total number of taxa regardless of their classification level. Alpha diversity at the species level was then calculated for the three samples using the total number of observed species, Shannon, and Inverse Simpson indexes after rarefaction at the number of reads of the sample with the lowest coverage. To ensure robustness of alpha diversity indexes, we also calculated the same indexes by filtering out singletons and taxa below 100 reads, rarefacting for the minimal sample depth in each case. Relative abundance compositions of the top 10 phyla across all Kingdoms and the top 10 species per Kingdom were investigated independently. As the biotic measures were taken from the Center for Cervical Cancer Elimination, where cervical samples from the screening program are received and analyzed, we aimed to assess the presence of Papillomavirus across locations specifically. Human papillomavirus characterization across samples was first evaluated with Kraken outputs and then using DIAMOND 17 (v2.0.15.153) using blastx and –top 1 options against PaVE database (Papillomavirus Episteme, https://pave.niaid.nih.gov/ , accessed on 2021-08-17) containing 3,599 human papillomaviruses with a threshold of minimum 10 reads with at least 90% identity and 750bp coverage. Abiotic profiling Access Sensor Technologies shiny app ( https://accsensors.shinyapps.io/shinyAST/ ) was used to explore the different components of the abiotic fraction (see variables at Exposometer devices subheading in the Methods section). The app summarized each sample, including the overall duration, pumping duration, pumping flow average factory, overall flow average factory, sampled volume factory, pumping flow average offset, overall flow average offset, sampled volume offset, and shutdown reason. Details on sample settings and device operation were also available for each sample within the shiny app. The app also offers the possibility to explore interactive plots and GPS maps and generate predefined reports. This work presents an example of reports generated by the app for direct-read sensors, including particulate matter ≤2.5µm (PM2_5MC), lux, carbon dioxide (CO 2 ), volatile organic compounds (VOCRaw), and nitrogen oxides (NOXRaw) over sample time (in hours) for all sample locations (Fig. 3 ). Declarations Acknowledgements We would like to thank Professor Snyder and his team at Stanford University for advice on the exposometer devices. We also acknowledge Jay Bowerss at Access Sensors Technology for his guidance on getting started with the exposometer devices. Author contributions J.D conceived the study, provided funding and supervision. V.N.P provided filter material and advised on the exposometer preparation and extraction protocol. R.M and H.A coordinated the reception of exposometer devices, managed their usage within the study and prepared the abiotic data. C.L performed extraction and sequencing of the samples for the biotic fraction. A.G.S conducted the data analysis and wrote the original manuscript. All authors read and approved the final manuscript. Data availability statement Data obtained from all sample locations is openly available in GitHub: https://github.com/HEAP-EXPOSOME/UPAS-environmental-data. Pipelines used within this manuscript can be found in the European Human Exposome Network (EHEN) toolbox: https://www.humanexposome.eu/2024/10/15/biopipe-a-parallelised-pipeline-for-taxonomy-classification-in-rna-dna-sequencing-data/ and https://www.humanexposome.eu/2024/10/15/hpv-meta-bioinformatics-pipeline-for-hpv-detection/. Funding information This work was funded by the Human Exposome Assessment Platform (Project No. 874662) granted by Horizon 2020 Framework Programme. Open access funding was provided by Karolinska Institutet. Competing interests The authors declare no competing interests. References Vermeulen, R., Schymanski, E. L., Barabási, A. L. & Miller, G. W. 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D. dplyr: A Grammar of Data Manipulation. Preprint at (2023). Tables Table 1. Alpha diversity (Observed species, Shannon, and Inverse Simpson indexes) at species level of all sample locations after rarefaction at 1.38M reads (taxa ³10 reads), 1.39M reads (taxa >1 reads), and 1.33M (taxa ³100 reads). Taxa ³ 10 reads Observed species Shannon Inverse Simpson Clean room 4,388 4.43 17.22 Analysis room 4,090 4.46 16.91 Sample reception room 3,545 4.60 37.19 Taxa >1 reads Observed species Shannon Inverse Simpson Clean room 6,584 4.48 17.43 Analysis room 5,793 4.50 17.07 Sample reception room 5,025 4.63 37.40 Taxa ³ 100 reads Observed species Shannon Inverse Simpson Clean room 1,163 4.15 16.00 Analysis room 1,114 4.24 16.00 Sample reception room 1,014 4.43 35.40 Table 2. Raw counts in each of the three locations for the 10 most abundant phyla and other phyla grouped by Kingdom. Clean room Analysis room Sample reception room Archaea-Other 6,056 2,915 408 Archaea-Euryarchaeota 5,603 8,642 23,188 Bacteria-Other 36,181 33,649 36,526 Bacteria-Actinomycetota 334,998 83,458 62,396 Bacteria-Bacillota 84,710 126,005 149,889 Bacteria-Bacteroidota 75,817 27,263 43,987 Bacteria-Campylobacterota 37,396 10,793 2,560 Bacteria-Pseudomonadota 299,127 269,909 244,130 Eukaryota-Other 9,169 16,343 3,229 Eukaryota-Apicomplexa 309,619 31,781 23,962 Eukaryota-Ascomycota 319,419 1,040,020 855,049 Eukaryota-Basidiomycota 64,682 176,656 196,701 Eukaryota-Euglenozoa 16,386 41,138 2,031 Viruses-Other 4,534 10,634 3,522 Total reads 1,603,697 1,879,206 1,647,578 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8058538","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":550811027,"identity":"60fb01e8-782b-4848-ab5f-e585e1f5fb9b","order_by":0,"name":"Ainhoa Garcia-Serrano","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Ainhoa","middleName":"","lastName":"Garcia-Serrano","suffix":""},{"id":550811028,"identity":"e4b39d5e-e44b-4c65-8f33-7654a5be405b","order_by":1,"name":"Helena Andersson","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Helena","middleName":"","lastName":"Andersson","suffix":""},{"id":550811029,"identity":"1aadfa42-f095-4f3d-b641-73e63b7d3dcb","order_by":2,"name":"Camilla Lagheden","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Camilla","middleName":"","lastName":"Lagheden","suffix":""},{"id":550811030,"identity":"806d9598-fe6a-424a-9896-a50fe29a7cce","order_by":3,"name":"Ville N. Pimenoff","email":"","orcid":"","institution":"University of Oulu","correspondingAuthor":false,"prefix":"","firstName":"Ville","middleName":"N.","lastName":"Pimenoff","suffix":""},{"id":550811031,"identity":"7ab171bf-d0d9-43b3-b861-ee42b7039ec7","order_by":4,"name":"Roxana Merino-Martinez","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Roxana","middleName":"","lastName":"Merino-Martinez","suffix":""},{"id":550811032,"identity":"1bfc9c57-1474-4a55-b53f-9f7ff39e7eac","order_by":5,"name":"Joakim Dillner","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACxgYGZoYEIN3GztwA5NsYsBGvhZkRpCWNsBYgYIZohWg5bEBYfXvzY4OHOQyyfcyMbQ8+7jlvzMfA/vABXof1HDNOSNzGYAx0WLvhjGe3zdgYeIzxWsU4I8H4AFBLIlBLmzTPgds2QC1sEvi1pH9GaPlz4BxQC/vzH/i15IAdBtHCcOAA0GEMZvh0AP1yptggcZsEyC9tkj0Hko3ZmHmM8TrMsL19s+TPbTay89ubj0n8OGBnOL+9/eEHvFoawBSyscx4ncXAIE9AfhSMglEwCkYBAwMALZRDNdjtOlwAAAAASUVORK5CYII=","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":true,"prefix":"","firstName":"Joakim","middleName":"","lastName":"Dillner","suffix":""}],"badges":[],"createdAt":"2025-11-07 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13:37:27","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83612,"visible":true,"origin":"","legend":"","description":"","filename":"db2dacd171d54823abc71b3c352debf51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8058538/v1/96ef90007160ad3022f5aad9.xml"},{"id":96829469,"identity":"e16cada3-45bd-4dcc-856a-8bcf16ccceb5","added_by":"auto","created_at":"2025-11-26 13:37:27","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91710,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8058538/v1/11147b6bd4ee650bb3f0b920.html"},{"id":96917417,"identity":"33b2b793-e2bf-4068-81ac-78749d481df7","added_by":"auto","created_at":"2025-11-27 14:09:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150889,"visible":true,"origin":"","legend":"\u003cp\u003eTaxa composition across all locations (clean room, analysis room, sample reception room) including a) shared and unique taxa among locations and b) relative abundance of top 10 phyla for all locations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8058538/v1/38796a47bdab52207137f69c.png"},{"id":96829452,"identity":"0bde3b88-033b-406b-a0a9-b3e36413a47f","added_by":"auto","created_at":"2025-11-26 13:37:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":269586,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of the 10 most abundant species among locations (clean room, analysis room, sample reception room) for a) archaea, b) bacteria, c) fungi and protists, and D) virus.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8058538/v1/08cc241b06a6b527d4780b6e.png"},{"id":96918272,"identity":"c3456cb3-3a2a-45a3-8770-9e8299fd4e12","added_by":"auto","created_at":"2025-11-27 14:11:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":156290,"visible":true,"origin":"","legend":"\u003cp\u003eGenerated reports for direct-read sensors including particulate matter £2.5mm (PM2_5MC), lux, carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), volatile organic compounds (VOCRaw), and nitrogen oxides (NOXRaw) over sample time (hours) for a) the clean room (renrummet), b) the analysis room (BD rummet), and c) the sample reception (provmottagning).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8058538/v1/afc037d599a89f14f5598806.png"},{"id":98425130,"identity":"a218ead0-1f84-40dd-b3a7-925ff3a6bf50","added_by":"auto","created_at":"2025-12-17 16:34:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1312882,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8058538/v1/7e3538e4-7f2a-42ce-8816-23b6d83c67dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exposometer-based assessment of laboratory work environment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe exposome includes all biological agents, chemical compounds, and physical factors present in the environment that impact human health\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The environmental exposures may vary even within individuals living in the same geographic area\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e making it challenging to assess all the exposures in the environment that may affect humans.\u003c/p\u003e\u003cp\u003eThe microbiome (all microorganisms that humans are exposed to) is an intensely studied part of the expsome\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Microorganisms are key modulators of human health, as commensals and health-detrimental factors\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.Environmental microbiome research has mainly focused on soil and water ecosystems, while airborne microbes have been understudied\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Direct inhalation of microbes, which can integrate into the respiratory microbiome, could modulate the susceptibility to inhaled irritants\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e while inhaled compounds could also alter respiratory microbiome profiles\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. On the other hand, abiotic exposures (physical and chemical) have also been extensively studied, especially for radiation, gas concentrations, or particles. Still, fewer studies focused on the exposure to airborne microbes and even fewer on the combination of both biotic and abiotic factors in the environment\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWork environment exposures constitute a crucial part of the exposome for an average adult; therefore, it is critical to ensure a healthy and safe environment at work. Particularly for laboratories, exposures can be more hazardous as laboratories commonly handle biological samples as well as health-detrimental chemical compounds. Recent developments in exposometer devices have been key to shifting the exposure studies towards a more comprehensive characterization, which allows both the integration of multiple data types\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and the development of monitoring systems for highly specialized applications\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The exhaustive study of the human exposome will substantially improve the understanding of how the environment shapes our health and will be of special interest in the context of exposure monitoring. This work aims to present a proof-of-concept on biotic and abiotic monitoring of exposures in a work environment.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cu\u003eOverall taxonomic classification\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eHuman DNA removal was performed in two steps: a) using NextGenMap, and b) using kraken2. NextGenMap identified a total of 3.8M human reads (1.24% of all reads) in the clean room, 10.9M human reads (3.60% of all reads) in the analysis room, and a total of 38.24M human reads (12.33% of all reads) in the sample reception. After the first step of human DNA removal, kraken2 identified 27.45M, 55.67M, and 41.83M human reads in the clean room, analysis room, and sample reception. Reads classified as known microorganisms corresponded to 0.53% of all initial reads for the clean room, 0.62% for the analysis room, and 0.53% for the sample reception. Overall, 0.56% of all reads across sample locations were classified as known microorganisms, comprising a total of 5.15M reads representing 10,910 different taxa. Taxa with less than 10 reads were then filtered out in order to remove potential sequencing artifacts, resulting in a total of 5,738 taxa. From these, the vast majority of taxa belonged to bacteria (n=5,205), followed by archaea (n=213), eukaryotes (n=187), and viruses (n=133). Relative abundance among remaining taxa ranged between 0.000195% and 8.13%. The total reads classified as non-human, at any taxonomic level and with more than 10 reads, considered in the downstream analysis, was above 5.13M reads. From those, the analysis room presented 1.88M reads, followed by the sample reception with 1.65M reads and the clean room with 1.60M reads. The location with the most unique taxa was the clean room with 270 unique taxa, followed by the analysis room with 202, and the sample reception with 126. The three locations shared 3,183 taxa, while the clean room shared 541 with the sample reception and 1,013 with the analysis room. The sample reception and the analysis room shared 403 taxa. The number of taxa specific for each group and shared between groups is available in Figure 1A.\u003c/p\u003e\n\u003cp\u003eThe overall taxonomic resolution at the species level reached 92.04%, being the highest among bacteria (92.57%), followed by archaea (91.55%), viruses (90.98%), and eukaryotes (78.61%).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSpecies diversity and core microbiome composition\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eFiltering taxa with at least 10 reads, and after rarefaction at 1.38M reads, alpha diversity at the species-level revealed a higher number of total observed species in the clean room, with a total of 4,388 species. The analysis room and the sample reception obtained a total number of observed species of 4,090 and 3,545, respectively. In contrast, the highest Shannon and inverse Simpson indexes corresponded to the sample reception. Even though differences in location were not wide in Shannon index (4.60 in the sample reception, 4.46 in the analysis room, and 4.43 in the clean room), differences in inverse Simpson index were wider with respect to the highest value (37.19 in the sample reception, 17.22 in the clean room, and 16.91 in analysis room) as shown in Table 1. While the Shannon index focuses on richness and evenness, the Inverse Simpson index focuses on evenness and dominance of specific taxa, being less sensitive towards rare taxa than the Shannon index. The fact that the sample reception presents a much higher Inverse Simpson index suggests a more even distribution of dominant species than in the other locations. The abovementioned alpha diversity indices were also calculated after filtering taxa for \u0026gt;1 reads (rarefacted at 1.39M) and\u0026nbsp;³100 reads (rarefacted at 1.33M reads) to ensure robustness of the results, displaying the same diversity patterns across sample locations (Table 1).\u003c/p\u003e\n\u003cp\u003eWhen focusing on the main phylum across samples, we observed more similar compositions between the analysis room and the sample reception. In contrast, the phylum composition of the clean room was more differential. The clean room presented lower proportions of Ascomycota (fungi), Bacillota (bacteria), and Basidiomycota (fungi). In contrast, Actinomycetota (bacteria), Apicomplexa (protist), Bacteroidota (bacteria), Campylobacterota (bacteria), and Pseudomonadota (bacteria) presented higher proportions in the clean room in comparison to the other locations. Even though the analysis room and the sample reception presented more similar compositions, some differences were also observed for Ascomycota (fungi) and Euglenozoa (protist), which were more abundant in the analysis room. In contrast, Bacillota (bacteria), Bacteroidota (bacteria), Basidiomycota (fungi), and Euryachaeota (archaea) were higher in the sample reception (Figure 1B). The number of reads classified per phylum and sample location is available in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eKingdom-specific core species\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eArchaea\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMost abundant archaeal species were unique across locations (Figure 2A). The sample reception presented a clear dominance of \u003cem\u003eHalobaculum sp. DT55\u003c/em\u003e, followed by\u003cem\u003e\u0026nbsp;Haloterrigena turkmenica\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Halomicrobium urmianum,\u0026nbsp;\u003c/em\u003ewhile the most abundant archaeal species in the analysis room were \u003cem\u003eMethanosarcina siciliae\u0026nbsp;\u003c/em\u003efollowed by\u003cem\u003e\u0026nbsp;Methanosarcina barkeri\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Nanobdella aerobiophila\u003c/em\u003e. The clean room presented a clear dominance of \u003cem\u003eCandidatus Aciduliprofundum sp. MAR08-339\u003c/em\u003e, followed by\u003cem\u003e\u0026nbsp;Natromonas gomsonensis, Candidatus Prometheoarchaeum syntrophicum\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;Methanobrevidbacter olleyae.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBacteria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDominant bacterial species profiles were also divergent across sample locations (Figure 2B). However, some similarities were observed between the sample reception and the analysis room, sharing the presence of \u003cem\u003eCutibacterium acnes\u003c/em\u003e and \u003cem\u003eStreptococcus thermophilus,\u003c/em\u003e and between the clean room and the analysis room, sharing \u003cem\u003eBacillus velezensis\u003c/em\u003e. Bacterial species \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eMoraxella osloensis\u003c/em\u003e were present across all three locations. Location-unique bacterial species were present only in the sample reception (\u003cem\u003eCaldinitratiruptor microaerophilus\u003c/em\u003e) and in the clean room (\u003cem\u003eAmycolatopsis sp NBC 01480\u003c/em\u003e, \u003cem\u003eKlebisella pneumoniae\u003c/em\u003e, \u003cem\u003ePseudoaltromonas undina\u003c/em\u003e, and \u003cem\u003eRhodococcus globerulus\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEukaryota\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEukaryotic species composition in the sample reception and the analysis room displayed more closely related profiles compared to the clean room (Figure 2C). However, all the dominant species in this Kingdom were present in all the sample locations. Major compositional differences included the high abundance of \u003cem\u003ePlasmodium coatneyi\u003c/em\u003e and \u003cem\u003eAspergillus luchiensis\u003c/em\u003e in the clean room, \u003cem\u003eZymoseptoria tritici\u003c/em\u003e and \u003cem\u003eFluvia fulva\u003c/em\u003e in the analysis room, and \u003cem\u003eBotrytis cinerea\u003c/em\u003e and\u0026nbsp;\u003cem\u003ePrycularia oryzar\u003c/em\u003e in the sample reception, even though these two last species did not present as relevant differences across locations as the others.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVirus\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe composition of the most abundant viral species among sample locations presented different profiles (Figure 2D). While the analysis room and the sample reception shared the main viral species (\u003cem\u003eNanovirus Black medic leaf roll virus\u003c/em\u003e), the clean room presented a predominance of \u003cem\u003eAlphabaculovirus agipsilonis\u003c/em\u003e, which was not present in the other locations. Other unique viral species only found in the clean room were \u003cem\u003ePinus nigra virus 1\u003c/em\u003e, \u003cem\u003eFowl aviadenovirus C,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Megrivirus Hubei picorna-like virus 24\u003c/em\u003e. The sample reception presented two unique viral species: \u003cem\u003ePandoravirus neocaledonia\u003c/em\u003e and \u003cem\u003eTupanvirus soda lake\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003ePapillomavirus across sample locations\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe Papillomaviridae family presented four different genera according to unfiltered Kraken2 results across samples, namely \u003cem\u003eAlphapapillomavirus\u003c/em\u003e, \u003cem\u003eDeltapapillomavirus\u003c/em\u003e, \u003cem\u003eGammapapillomavirus\u003c/em\u003e and \u003cem\u003ePsipapillomavirus\u003c/em\u003e. However, when filtering out taxa with less than 10 reads, only \u003cem\u003eGammapapillomavirus\u003c/em\u003e remained, suggesting that the other genera were probably sequencing artifacts. Further analyses for Human Papillomavirus-specific characterization using DIAMOND\u003csup\u003e17\u003c/sup\u003e revealed that even though all raw samples had reads aligning against HPV, none of the locations presented more than 10 reads with at least a 90% identity. However, the sample reception was the only location presenting reads with ≥90% identity, with three reads above the identity threshold for HPV-mSK152, but only 108bp coverage. We can therefore conclude that none of the three locations presented environmental Papillomavirus.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAbiotic exposures\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAbiotic exposures included PM2.5MC, LUX, CO\u003csub\u003e2\u003c/sub\u003e, VOCRaw, and NOXRaw, monitored for 7 days for the three locations. The majority of exposures displayed rhythmic variations according to workday schedules. Focusing on PM2.5MC, the pattern was exactly the same in the three locations, with a high peak during the night of the second day to the third day, followed by a more discrete peak the next night, and at the end of the study period. Light also followed diurnal patterns, with notable differences between the three locations. Regarding CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eand VOCRaw, we also found diurnal patterns that were opposite in this case: higher CO\u003csub\u003e2\u003c/sub\u003e during the day and higher VOCRaw during nights. Finally, NOXRaw was incremental over time with differential patterns depending on the sample location.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMost research on human health exposures has focused on the abiotic fraction (radiation, gas concentrations, particles, among others) or the biotic fraction separately\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, studies that integrate the characterization of both types of exposures are scarce\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The present work serves as a proof-of-concept for environmental studies using exposome data, including both biotic and abiotic fractions, from a use case in a laboratory work environment.\u003c/p\u003e\u003cp\u003eResults obtained within the study characterized different biotic compositions across sample locations, which were even noticeable at the phylum level. When focusing on the species level, \u003cem\u003eCutibacterium acnes\u003c/em\u003e, a commensal bacteria found on human skin\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, was found as one of the most abundant species in both the sample reception and the analysis room. In contrast, the clean room presented negligible amounts of this bacterial species as the protocols for decontamination were stricter than in the other rooms. These findings agreed with the percentage of removed human DNA over the total reads, which was the lowest in the clean room. In contrast, the clean room presented a strong presence of \u003cem\u003eAmycolatopsis sp NBC 01480\u003c/em\u003e bacterial species. Other \u003cem\u003eAmycolatopsis\u003c/em\u003e species have been previously described as spore-forming microorganisms surviving sterilization in clean rooms at NASA\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, evidencing the challenge of eliminating these bacteria even in highly restrictive decontamination conditions. Samples used in the study were taken from different locations within the Center for Cervical Cancer Elimination (CCCE), where clinical samples for HPV genotyping screening tests are received, processed, and analyzed. The CCCE holds the National and International Reference Laboratories for HPV. For this reason, we aimed to investigate the presence of the Papillomavirus family among locations using both the main microbiome characterization pipeline and another specific pipeline for Human Papillomavirus (HPV) detection. Even though some Papillomavirus reads were classified with both of the pipelines, none surpassed the cut-offs established to consider its presence in any of the locations, implying absence of potential contamination of PCR-derived HPV DNA in laboratory room aerosols and therefore also the absence of HPV risk exposures in the work environment.\u003c/p\u003e\u003cp\u003eThe study displayed temporal and spatial differences when focusing on the abiotic fraction. Clear rhythmic daily variations were observed for LUX, CO\u003csub\u003e2\u003c/sub\u003e and VOCRaw. At the same time, both LUX and CO\u003csub\u003e2\u003c/sub\u003e increased during working hours (day) (with some location variations due to different natural light conditions and different flow of workers in each of the rooms), VOCRaw increased after working hours (night) in all locations, with some variations.\u003c/p\u003e\u003cp\u003eThe main limitation of this study is the number of samples taken. Since the present work aimed to serve as a use case and not a comprehensive comparison between location exposures, only one device was used per location (n = 3). Moreover, the biotic fraction represented a snapshot at the end of the measurement period, and there is no information on how these biotic exposures varied over time, as we have this information only for the abiotic fraction. It is important to note that the exposometers' data were collected during a week in June 2025; thus, the results must be interpreted considering the data's seasonality. Multiple studies have shown variations in environmental microorganisms depending on the season\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Hence, the biotic compositions for the locations described might vary depending on when the measures are taken.\u003c/p\u003e\u003cp\u003eEven though the present work serves as a proof-of-concept, as most of the studies conducted using exposometers to date\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, the results presented here confirm the potential of these devices to be implemented in different settings for a variety of use cases such as hospital monitoring (ICU, nosocomial infections, or environmental laboratory safety monitoring), environmental microbiome surveillance (outbreaks, ecosystem changes, seasonal infections), or in public health control (health-detrimental exposures in a specific region or area). The information obtained from the devices could also be integrated with other real-time data sources to capture a broader range of data types, enabling multimodal data integration and offering a more comprehensive overview of exposures. Additionally, advanced analytical techniques such as mass spectrometry could be employed to achieve a more precise molecular characterization of the exposures.\u003c/p\u003e\u003cp\u003eThe present work is a case study of how stationary exposometer devices could inform about both biotic and abiotic exposures in specific locations, providing a powerful tool for monitoring potential health-detrimental factors in different environments.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003eExposometer devices\u003c/h2\u003e\u003cp\u003eStationary exposometer devices were used to measure both abiotic and biotic exposures in 3 different work environment locations within the Center of Cervical Cancer Elimination at Karolinska Institutet (Stockholm, Sweden) for 7 days (9th to 16th June 2025). The devices were placed in the 1) sample reception, where HPV screening tests are received and registered, 2) in a pre-PCR room named the clean room, and 3) in the analysis room, where the PCR machines used for HPV genotyping are located. The exposometer devices registered information for fine particulate matter mass concentration, CO\u003csub\u003e2\u003c/sub\u003e concentration, VOCs and NOx, light (Lux, IR, UV, UVindex), pressure, relative humidity, sample flow rate, and differential pressure across the sample filter. The biotic fractions were captured from the filters at the end of the measurements. Device product information and user guide from Access Sensor Technologies are available in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.accsensors.com/upasv2.1plus\u003c/span\u003e\u003cspan address=\"https://www.accsensors.com/upasv2.1plus\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eFilters extraction and sequencing\u003c/h2\u003e\u003cp\u003eExposometer filters were cut with sterile scissors into smaller pieces and introduced into a tube with BD diluent at room temperature for 1 hour. Afterward, each filter was extracted in 4 tubes of 200µl each, eluted in 50µl and finally pooled into one tube per filter.\u003c/p\u003e\u003cp\u003eExtraction was performed using the extraction kit Magtration Reagent MagDEA Dx SV (Precision System Science, PSS) in MagLead and followed by DNA amplification using REPLI-g Single Cell kit (Qiagen) according to the manufacturer's reference guide with 17h of incubation at 30°C. After incubation, the samples were inactivated for 3 minutes at 65°C. Amplified material was thereafter subjected to library preparation using SMARTer Stranded Total RNA-Seq Kit v2 (Takara Bio USA) library preparation kit following the manufacturer's reference guide. The libraries were validated, normalized to 2 nM, and pooled. Pair-end sequencing, 2x150 cycles, was performed using NextSeq550 (Illumina, San Diego, CA).\u003c/p\u003e\u003ch2\u003eBiotic profiling\u003c/h2\u003e\u003ch2\u003eBioinformatic preprocessing\u003c/h2\u003e\u003cp\u003eSequence reads obtained from NextSeq500 (Illumina) were assigned to the samples using the indexes in the adapters. Quality trimming and adaptor removal were performed using Trimmomatic\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (v0.39) with a minimal length of 18bp and using the option keepBothReads. Then, human reads were removed using NextGenMap\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e (v0.5.5) using 95% of identity and 75% of the read length against GRCh38 and GCA-009914755.4-T2T-CHM13v2.0 human references. Non-human reads were retrieved, merged by sample, sorted, and converted to fastq files using samtools (v1.6).\u003c/p\u003e\u003ch2\u003eMicrobiome profiling\u003c/h2\u003e\u003cp\u003eTaxonomic classification of remaining reads was performed using Kraken2\u003csup\u003e23\u003c/sup\u003e (v2.1.2) against all RefSeq curated genomes for archaea, bacteria, viruses, protozoa, and fungi, as well as human, plasmids and univec potential contaminants (database obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://benlangmead.github.io/aws-indexes/k2\u003c/span\u003e\u003cspan address=\"https://benlangmead.github.io/aws-indexes/k2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, collection PlusPF). Reports generated by kraken2 were transformed to biom files using the kraken2biom tool\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and then imported to R (v4.2.2). Downstream microbiome analyses were conducted using the following packages: phyloseq\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, ggplot2\u003csup\u003e26\u003c/sup\u003e, tidytacos\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and dplyr\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The total number of taxa and reads across all samples was evaluated after removing human and plasmid reads. Taxa with fewer than 10 reads were filtered out to remove potential sequencing artifacts. Taxonomic resolution was assessed at the species level by dividing the total number of taxa classified at the species level by the total number of taxa regardless of their classification level. Alpha diversity at the species level was then calculated for the three samples using the total number of observed species, Shannon, and Inverse Simpson indexes after rarefaction at the number of reads of the sample with the lowest coverage. To ensure robustness of alpha diversity indexes, we also calculated the same indexes by filtering out singletons and taxa below 100 reads, rarefacting for the minimal sample depth in each case. Relative abundance compositions of the top 10 phyla across all Kingdoms and the top 10 species per Kingdom were investigated independently.\u003c/p\u003e\u003cp\u003eAs the biotic measures were taken from the Center for Cervical Cancer Elimination, where cervical samples from the screening program are received and analyzed, we aimed to assess the presence of Papillomavirus across locations specifically. Human papillomavirus characterization across samples was first evaluated with Kraken outputs and then using DIAMOND\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e (v2.0.15.153) using blastx and –top 1 options against PaVE database (Papillomavirus Episteme, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pave.niaid.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pave.niaid.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 2021-08-17) containing 3,599 human papillomaviruses with a threshold of minimum 10 reads with at least 90% identity and 750bp coverage.\u003c/p\u003e\u003ch2\u003eAbiotic profiling\u003c/h2\u003e\u003cp\u003eAccess Sensor Technologies shiny app (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://accsensors.shinyapps.io/shinyAST/\u003c/span\u003e\u003cspan address=\"https://accsensors.shinyapps.io/shinyAST/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to explore the different components of the abiotic fraction (see variables at Exposometer devices subheading in the Methods section). The app summarized each sample, including the overall duration, pumping duration, pumping flow average factory, overall flow average factory, sampled volume factory, pumping flow average offset, overall flow average offset, sampled volume offset, and shutdown reason. Details on sample settings and device operation were also available for each sample within the shiny app. The app also offers the possibility to explore interactive plots and GPS maps and generate predefined reports. This work presents an example of reports generated by the app for direct-read sensors, including particulate matter ≤2.5µm (PM2_5MC), lux, carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), volatile organic compounds (VOCRaw), and nitrogen oxides (NOXRaw) over sample time (in hours) for all sample locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Professor Snyder and his team at Stanford University for advice on the exposometer devices. We also acknowledge Jay Bowerss at Access Sensors Technology for his guidance on getting started with the exposometer devices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.D conceived the study, provided funding and supervision. V.N.P provided filter material and advised on the exposometer preparation and extraction protocol. R.M and H.A coordinated the reception of exposometer devices, managed their usage within the study and prepared the abiotic data. C.L performed extraction and sequencing of the samples for the biotic fraction. A.G.S conducted the data analysis and wrote the original manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData obtained from all sample locations is openly available in GitHub: https://github.com/HEAP-EXPOSOME/UPAS-environmental-data. Pipelines used within this manuscript can be found in the European Human Exposome Network (EHEN) toolbox: https://www.humanexposome.eu/2024/10/15/biopipe-a-parallelised-pipeline-for-taxonomy-classification-in-rna-dna-sequencing-data/ and https://www.humanexposome.eu/2024/10/15/hpv-meta-bioinformatics-pipeline-for-hpv-detection/. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Human Exposome Assessment Platform (Project No. 874662) granted by Horizon 2020 Framework Programme. Open access funding was provided by Karolinska Institutet.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVermeulen, R., Schymanski, E. L., Barab\u0026aacute;si, A. L. \u0026amp; Miller, G. W. 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Precision environmental health monitoring by longitudinal exposome and multi-omics profiling. \u003cem\u003eGenome Res.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 1199\u0026ndash;1214 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, Z. et al. Longitudinal Mapping of Personal Biotic and Abiotic Exposomes and Transcriptome in Underwater Confined Space Using Wearable Passive Samplers. \u003cem\u003eEnviron. Sci. Technol.\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e, 5229\u0026ndash;5243 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrasier, K., Li, V., Sobotka, M., Vinagolu-Baur, J. \u0026amp; Herrick, G. The role of wearable technology in real‐time skin health monitoring. \u003cem\u003eJEADV Clin. Pract.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 21\u0026ndash;29 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuchfink, B., Reuter, K. \u0026amp; Drost, H. G. Sensitive protein alignments at tree-of-life scale using DIAMOND. \u003cem\u003eNat. Methods\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 366\u0026ndash;368 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlmoughrabie, S. et al. Commensal Cutibacterium acnes induce epidermal lipid synthesis important for skin barrier function. \u003cem\u003eSci Adv\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDanko, D. C. et al. A comprehensive metagenomics framework to characterize organisms relevant for planetary protection. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 82 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, X. et al. Dynamic airborne mycobiome in the metropolitan city transit system is driven by seasonality and station type. \u003cem\u003eMicrobiol. 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Improved metagenomic analysis with Kraken 2. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 257 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDabdoub, S. kraken-biom: Enabling interoperative format conversion for Kraken results. Preprint at (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcMurdie, P. J. \u0026amp; Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, e61217 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWickham, H. \u003cem\u003eggplot2: Elegant Graphics for Data Analysis\u003c/em\u003e (Springer-, 2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWittouck, S., Rillaer, T., Van, Smets, W., Lebeer, S. \u0026amp; Tidytacos An R package for analyses on taxonomic composition of microbial communities. \u003cem\u003eJ. Open. 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Preprint at (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eAlpha diversity (Observed species, Shannon, and Inverse Simpson indexes) at species level of all sample locations after rarefaction at 1.38M reads (taxa \u0026sup3;10 reads), 1.39M reads (taxa \u0026gt;1 reads), and 1.33M (taxa \u0026sup3;100 reads).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTaxa\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026sup3;\u003c/strong\u003e\u003cstrong\u003e10 reads\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObserved species\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShannon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInverse Simpson\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClean room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4,388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e17.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4,090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e16.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample reception room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3,545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e37.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTaxa \u0026gt;1 reads\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObserved species\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShannon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInverse Simpson\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClean room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e6,584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e17.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e5,793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e17.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample reception room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e5,025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e37.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTaxa\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026sup3;\u003c/strong\u003e\u003cstrong\u003e100 reads\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObserved species\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShannon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInverse Simpson\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClean room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1,163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e16.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1,114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e16.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample reception room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e35.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eRaw counts in each of the three locations for the 10 most abundant phyla and other phyla grouped by Kingdom.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClean room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample reception room\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArchaea-Other\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6,056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2,915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArchaea-Euryarchaeota\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5,603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e8,642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e23,188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBacteria-Other\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e36,181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e33,649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e36,526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBacteria-Actinomycetota\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e334,998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e83,458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e62,396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBacteria-Bacillota\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e84,710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e126,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e149,889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBacteria-Bacteroidota\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e75,817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e27,263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e43,987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBacteria-Campylobacterota\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e37,396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e10,793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2,560\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBacteria-Pseudomonadota\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e299,127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e269,909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e244,130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEukaryota-Other\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e9,169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e16,343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e3,229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEukaryota-Apicomplexa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e309,619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e31,781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e23,962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEukaryota-Ascomycota\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e319,419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1,040,020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e855,049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEukaryota-Basidiomycota\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e64,682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e176,656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e196,701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEukaryota-Euglenozoa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e16,386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e41,138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2,031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eViruses-Other\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4,534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e10,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e3,522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal reads\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,603,697\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,879,206\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,647,578\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Human exposome, biological exposures, chemical exposures, physical exposures, exposometer devices","lastPublishedDoi":"10.21203/rs.3.rs-8058538/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8058538/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman health is impacted by a wide range of exposures, including biological, chemical, and physical factors, making it challenging to assess their combined environmental health effects comprehensively. Advances in wearable and stationary sensor devices enable systematic longitudinal measurement of biotic and abiotic exposures. In this pilot study at the Center for Cervical Cancer Elimination (Karolinska Institutet, Sweden), we deployed stationary exposometer devices in three different laboratory locations (the pre-PCR \"clean-room\", the molecular analysis room, and the sample management/reception room) to assess the indoor biotic and abiotic aerosol exposures. Of all biotic sequences obtained after amplification, 0.56% were classified as known microorganisms. The aerosol profile of the clean room differed markedly from the sample reception and the analysis rooms, which exhibited similar biotic profiles at both the phylum level (all kingdoms) and species level for eukaryotic and viral fractions. The laboratory is an international reference laboratory for Human Papillomavirus, but this virus was not detected in the work environment in any location. Abiotic exposures displayed diurnal dynamics, with higher daytime light and carbon dioxide levels and higher volatile organic compounds (VOC) levels at night. These results demonstrate the feasibility of using exposometers for detailed indoor aerosol exposure assessment in occupational settings.\u003c/p\u003e","manuscriptTitle":"Exposometer-based assessment of laboratory work environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 13:37:22","doi":"10.21203/rs.3.rs-8058538/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65869279-bc47-4beb-95f8-2a83011c9e81","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58609733,"name":"Earth and environmental sciences/Environmental sciences"},{"id":58609734,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-12-11T17:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 13:37:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8058538","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8058538","identity":"rs-8058538","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}