Abstract
Odor pollution, also referred to as odor nuisance, is a growing environmental concern that is significantly associated with mental health. Once emitted into the air, the concentration of odorous substances varies considerably with wind conditions, leading to difficulties in timely sampling. In the present study, we employed selected ion flow tube mass spectrometry (SIFT-MS) to measure 22 odor-producing molecules continuously in an urban–rural complex city. In addition, we applied statistical analyses, principal component analysis (PCA), and a conditional probability function (CPF) to the datasets obtained from SIFT-MS to identify the odor characteristics at two study sites. At site A, odorants related to livestock farming and industry showed high factor loadings on principal components (PCs) from the PCA. In contrast, we estimated that the odorous gaseous chemicals affecting site B were closely related to sewage treatment and municipal solid waste disposal. Similar CPF patterns of grouped substances from the PCA supported the association between potential odor sources and specific odorants at site B, which helped estimate possible source locations. Consequently, our findings indicate that continuous monitoring of odorous substances using SIFT-MS can be an effective way to provide sufficient information on odor-producing molecules, leading to the clear identification of odor characteristics despite the high variability of odorous substances.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
No datasets were generated or analysed during the current study.
References
Blanes-Vidal, V., Nadimi, E. S., Ellermann, T., Andersen, H. V., & Løfstrøm, P. (2012). Perceived annoyance from environmental odors and association with atmospheric ammonia levels in non-urban residential communities: A cross-sectional study. Environmental Health, 11(1), 1–10. https://doi.org/10.1186/1476-069X-11-27
Bortone, I., Carrillo, S., Di Nardo, A., Di Natale, M., & Musmarra, D. (2012). Mitigation of the odorous impact of a waste landfill located in a highly urbanized area. Chemical Engineering, 28. https://doi.org/10.3303/CET1228039
Du, L., & Turner, J. (2015). Using PM2.5 lanthanoid elements and nonparametric wind regression to track petroleum refinery FCC emissions. Science of the Total Environment, 529, 65–71. https://doi.org/10.1016/j.scitotenv.2015.05.034
Fang, J., Zhang, H., Yang, N., Shao, L., & He, P. (2013). Gaseous pollutants emitted from a mechanical biological treatment plant for municipal solid waste: Odor assessment and photochemical reactivity. Journal of the Air & Waste Management Association, 63(11), 1287–1297. https://doi.org/10.1080/10962247.2013.822439
Guan, X., Rubin, E., & Anni, H. (2012). An optimized method for the measurement of acetaldehyde by high‐performance liquid chromatography. Alcoholism: Clinical and Experimental Research, 36(3), 398–405. https://doi.org/10.1111/j.1530-0277.2011.01612.x
Halket, J. M., Waterman, D., Przyborowska, A. M., Patel, R. K., Fraser, P. D., & Bramley, P. M. (2005). Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS. Journal of Experimental Botany, 56(410), 219–243. https://doi.org/10.1093/jxb/eri069
He, P.-J., Tang, J.-F., Yang, N., Fang, J.-J., He, X., & Shao, L.-M. (2012). The emission patterns of volatile organic compounds during aerobic biotreatment of municipal solid waste using continuous and intermittent aeration. Journal of the Air & Waste Management Association, 62(4), 461–470. https://doi.org/10.1080/10962247.2012.658954
Heaney, C. D., Wing, S., Campbell, R. L., Caldwell, D., Hopkins, B., Richardson, D., & Yeatts, K. (2011). Relation between malodor, ambient hydrogen sulfide, and health in a community bordering a landfill. Environmental Research, 111(6), 847–852. https://doi.org/10.1016/j.envres.2011.05.021
Hu, R., Liu, G., Zhang, H., Xue, H., Wang, X., & Lam, P. K. S. (2020). Odor pollution due to industrial emission of volatile organic compounds: A case study in Hefei. China. Journal of Cleaner Production, 246, 119075. https://doi.org/10.1016/j.jclepro.2019.119075
Imai, N., Osanai, A., Moriya, A., Katsuki, M., & Kitamura, E. (2023). Classification of odors associated with migraine attacks: A cross-sectional study. Scientific Reports, 13(1), 8469. https://doi.org/10.1038/s41598-023-35211-7
Kadohisa, M. (2013). Effects of odor on emotion, with implications. Frontiers in Systems Neuroscience, 7, 66. https://doi.org/10.3389/fnsys.2013.00066
Kherif, F., & Latypova, A. (2020). Principal component analysis. Machine Learning, 209–225. https://doi.org/10.1016/B978-0-12-815739-8.00012-2.
Kim, K.-H., Hong, Y.-J., Pal, R., Jeon, E.-C., Koo, Y.-S., & Sunwoo, Y. (2008). Investigation of carbonyl compounds in air from various industrial emission sources. Chemosphere, 70(5), 807–820. https://doi.org/10.1016/j.chemosphere.2007.07.025
Kimura, M., Akimoto, T., Kato, S., Hirasuga, N., Sakamoto, Y., Yamakita, S., & Sakakibara, H. (2019). Investigation of toilets with reduced ventilation frequencies and odor simulation. IOP Conference Series: Earth and Environmental Science, 294, 012046. https://doi.org/10.1088/1755-1315/294/1/012046
Langford, V. S. (2023). SIFT-MS: Quantifying the volatiles you smell… and the toxics you don’t. Chemosensors, 11(2), 111. https://doi.org/10.3390/chemosensors11020111
Luttrell, W. E., & Bellcock, L. R. (2015). Methyl ethyl ketone. Journal of Chemical Health & Safety, 22(4), 33–36. https://doi.org/10.1016/j.jchas.2015.06.007
Mateus, V. L., & Gioda, A. (2017). A candidate framework for PM2.5 source identification in highly industrialized urban-coastal areas. Atmospheric Environment, 164, 147–164. https://doi.org/10.1016/j.atmosenv.2017.05.025
McCrory, D., & Hobbs, P. (2001). Additives to reduce ammonia and odor emissions from livestock wastes: A review. Journal of Environmental Quality, 30(2), 345–355. https://doi.org/10.2134/jeq2001.302345x
McEwan, M. J. (2015). Direct analysis mass spectrometry. Ion/Molecule Attachment Reactions: Mass Spectrometry, 263–317. https://doi.org/10.1007/978-1-4899-7588-1_8
Mori, K., & Sakano, H. (2021). Olfactory circuitry and behavioral decisions. Annual Review of Physiology, 83, 231–256. https://doi.org/10.1146/annurev-physiol-031820-092824
Mukherjee, A., & Agrawal, M. (2018). Assessment of local and distant sources of urban PM2.5 in middle Indo-Gangetic plain of India using statistical modeling. Atmospheric Research, 213, 275–287. https://doi.org/10.1016/j.atmosres.2018.06.014
Mutegoa, E., & Sahini, M. G. (2023). Approaches to mitigation of hydrogen sulfide during anaerobic digestion process–A review. Heliyon, 9, e19768. https://doi.org/10.1016/j.heliyon.2023.e19768
Park, M.-B., Lee, T.-J., Lee, E.-S., & Kim, D.-S. (2019). Enhancing source identification of hourly PM2.5 data in Seoul based on a dataset segmentation scheme by positive matrix factorization (PMF). Atmospheric Pollution Research, 10(4), 1042–1059. https://doi.org/10.1016/j.apr.2019.01.013
Pekney, N. J., Davidson, C. I., Zhou, L., & Hopke, P. K. (2006). Application of PSCF and CPF to PMF-modeled sources of PM2.5 in Pittsburgh. Aerosol Science and Technology, 40(10), 952–961. https://doi.org/10.1080/02786820500543324
Peu, P., Picard, S., Diara, A., Girault, R., Béline, F., Bridoux, G., & Dabert, P. (2012). Prediction of hydrogen sulphide production during anaerobic digestion of organic substrates. Bioresource Technology, 121, 419–424. https://doi.org/10.1016/j.biortech.2012.06.112
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Reijer, A. H. d., Otter, P. W., & Jacobs, J. P. (2024). An heuristic scree plot criterion for the number of factors. Statistical Papers, 1–10. https://doi.org/10.1007/s00362-023-01517-x
Roberts, I. J., Carpenter, L. J., Shaw, M. D., & Langford, V. S. (2022). Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) study of the reactions of H3O+, NO+ and O2+ with a range of oxygenated volatile organic carbons (OVOCs). International Journal of Mass Spectrometry, 479, 116892. https://doi.org/10.1016/j.ijms.2022.116892
Sachin, D. (2015). Dimensionality reduction and classification through PCA and LDA. International Journal of Computer Applications, 122(17), 4–8. https://doi.org/10.5120/21790-5104
Sánchez-Ramírez, E., Contreras-Zarazua, G., Romero-García, A. G., Huerta-Rosas, B., Quiroz-Ramirez, J. J., & Segovia-Hernández, J. G. (2022). Production of methyl ethyl ketone applying process intensification strategies. Innovations in Fermentation and Phytopharmaceutical Technologies, 295–313. https://doi.org/10.1016/B978-0-12-821877-8.00021-X
Seo, Y.-K., & Baek, S.-O. (2011). Characterization of carbonyl compounds in the ambient air of an industrial city in Korea. Sensors, 11(1), 949–963. https://doi.org/10.3390/s110100949
Shusterman, D. (1999). The health significance of environmental odour pollution: Revisited. Journal of Environmental Medicine, 1(4), 249–258. https://doi.org/10.1002/jem.38
Smith, D., Španěl, P., Demarais, N., Langford, V. S., & McEwan, M. J. (2023). Recent developments and applications of selected ion flow tube mass spectrometry (SIFT‐MS). Mass spectrometry reviews, e21835. https://doi.org/10.1002/mas.21835
Sofowote, U. M., Su, Y., Dabek-Zlotorzynska, E., Rastogi, A. K., Brook, J., & Hopke, P. K. (2015). Sources and temporal variations of constrained PMF factors obtained from multiple-year receptor modeling of ambient PM2.5 data from five speciation sites in Ontario. Canada. Atmospheric Environment, 108, 140–150. https://doi.org/10.1016/j.atmosenv.2015.02.055
Sumonsiri, N., & Barringer, A. S. (2013). Application of SIFT-MS in monitoring volatile compounds in fruits and vegetables. Current Analytical Chemistry, 9(4), 631–641.
Tungkijanansin, N., Alahmad, W., Nhujak, T., & Varanusupakul, P. (2020). Simultaneous determination of benzoic acid, sorbic acid, and propionic acid in fermented food by headspace solid-phase microextraction followed by GC-FID. Food Chemistry, 329, 127161. https://doi.org/10.1016/j.foodchem.2020.127161
Uria-Tellaetxe, I., & Carslaw, D. C. (2014). Conditional bivariate probability function for source identification. Environmental Modelling & Software, 59, 1–9. https://doi.org/10.1016/j.envsoft.2014.05.002
Williams, J., Li, H., Ross, A. B., & Hargreaves, S. P. (2019). Quantification of the influence of NO2, NO and CO gases on the determination of formaldehyde and acetaldehyde using the DNPH method as applied to polluted environments. Atmospheric Environment, 218, 117019. https://doi.org/10.1016/j.atmosenv.2019.117019
Author information
Authors and Affiliations
Contributions
Sangcheol Kim supervised the study design; Sangcheol Kim and Taeryeong Choi contributed to data collection and verification; Sangcheol Kim performed data analyses and wrote the first draft of the manuscript; Sangcheol Kim, Taeryeong Choi, and Eunok Bang contributed to data interpretation.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kim, S., Choi, T. & Bang, E. Investigation of odor pollution by utilizing selected ion flow tube mass spectrometry (SIFT‐MS) and principal component analysis (PCA). Environ Monit Assess 196, 550 (2024). https://doi.org/10.1007/s10661-024-12708-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-024-12708-w