On new methods for measuring and identifying dust microparticles in ambient air
А.N. Kokoulin1, I.V. May2, S.Yu. Zagorodnov2, А.А. Yuzhakov1
1Perm National Research Polytechnic University, 29 Komsomolskiy Ave., Perm, 614990, Russian Federation
2Federal Scientific Center for Medical and Preventive Health Risk Management Technologies, 82 Monastyrskaya Str., Perm, 6140045, Russian Federation
Established health hazards posed by dust microparticles require automated and mobile devices for their assessment. Such devices should provide an opportunity to analyze component and disperse structures of the solid component in ambient air pollution operatively and in real time. In future, they will replace labor-consuming sampling and separate identification of fraction structure and chemical composition of dusts.
The aim of this study was to develop and test new methodical, procedural and instrumental approaches to monitoring of solid particles in ambient air. We suggest a hardware and software complex that implements a two-stage scheme for identifying solid particles sampled in ambient air according to the from-coarse-to-fine principle. The first stage involves identifying the total concentration of solid particles by laser diffraction. Microphotographs are taken with iMicro Q2 mini portable microscope with magnification x800. The microscope lens is connected to a camera, which is linked to nVidia Jetson Nano micro PC. The micro PC classifies particles, identifies their contours by using a neural network and deals with image segmentation. The second stage relies on using computer vision that makes it possible to automate routine recognition of particle images created by the microscope in order to calculate levels of different substances in a sample. All the data are analyzed by the second neural network that performs preset calculations in accordance with mathematical logic (model). The network is trained using a library that contains attributed microphotographs of dusts with different qualitative and disperse structures.
The algorithm has been tested with some promising results. Identified disperse structures and chemical composition of dusts turn out to be quite similar to those identified by conventional approaches and measurement methods. The method has been shown to offer wide opportunities to identify dust composition and structure, to create dust pollution profiles, and to estimate a contribution made by a specific source to overall pollution.
The study results ensure more correct and precise health risk assessment under exposure to dusts in ambient air.
- Curtis L., Rea W., Smith-Willis P., Fenyves E., Pan Y. Adverse health effects of outdoor air pollutants. Environ. Int., 2006, vol. 32, no. 6, pp. 815–830. DOI: 10.1016/j.envint.2006.03.012
- Treskova Yu.V. Otsenka stepeni opasnosti melkodispersnykh chastits v atmosfernom vozdukhe i tselesoobraznost' ikh normirovaniya [Assessment of the degree of danger of fine particles in the atmospheric air and the feasibility of their regulation]. Molodoi uchenyi, 2016, vol. 111, no. 7, pp. 291–294 (in Russian).
- Health effects of particulate matter final. WHO, 2013. Available at: https://www.euro.who.int/__data/assets/pdf_file/0006/189051/Health-effec... (December 27, 2022).
- Health effects of dust. Government of Western Australia, Department of Health. Available at: https://www.healthywa.wa.gov.au/Articles/F_I/Health-effects-of-dust (December 27, 2022).
- Revich B.A. Fine suspended particulates in ambient air and their health effects in megalopolises. Problemy ekologicheskogo monitoringa i modelirovaniya ekosistem, 2018, vol. 29, no. 3, pp. 53–78. DOI: 10.21513/0207-2564-2018-3-53-78 (in Russian).
- Liao Z., Nie J., Sun P. The impact of particulate matter (PM2.5) on skin barrier revealed by transcriptome analysis: Focusing on cholesterol metabolism. Toxicol. Rep., 2019, vol. 7, pp. 1–9. DOI: 10.1016/j.toxrep.2019.11.014
- Magnani N.D., Muresan X.M., Belmonte G., Cervellati F., Sticozzi C., Pecorelli A., Miracco C., Marchini T. [et al.]. Skin Damage Mechanisms Related to Airborne Particulate Matter Exposure. Toxicol. Sci., 2016, vol. 149, no. 1, pp. 227–236. DOI: 10.1093/toxsci/kfv230
- Peters R., Ee N., Peters J., Booth A., Mudway I., Anstey K.J. Air Pollution and Dementia: A Systematic Review. J. Alzheimers Dis., 2019, vol. 70, no. s1, pp. S145–S163. DOI: 10.3233/JAD-180631
- Choi H., Kim S.H. Air Pollution and Dementia. Dement. Neurocogn. Disord., 2019, vol. 18, no. 4, pp. 109–112. DOI: 10.12779/dnd.2019.18.4.109
- Lee M., Schwartz J., Wang Y., Dominici F., Zanobetti A. Long-term effect of fine particulate matter on hospitalization with dementia. Environmental Pollution, 2019, vol. 254, pt A, pp. 112926. DOI: 10.1016/j.envpol.2019.07.094
- Cserbik D., Chen J.-C., McConnell R., Berhane K., Sowell E.R., Schwartz J., Hackman D.A., Kan E. Fine particulate matter exposure during childhood relates to hemispheric-specific differences in brain structure. Environ. Int., 2020, vol. 143, pp. 105933. DOI: 10.1016/j.envint.2020.105933
- Katoto P.D.M.C., Brand A.S., Bakan B., Obadia P.M., Kuhangana C., Kayembe-Kitenge T., Kitenge J.P., Nkulu C.B.L. [et al.]. Acute and chronic exposure to air pollution in relation with incidence, prevalence, severity and mortality of COVID-19: a rapid systematic review. Environ. Health, 2021, vol. 20, no. 1, pp. 41. DOI: 10.1186/s12940-021-00714-1
- Comunian S., Dongo D., Milani C., Palestini P. Air Pollution and COVID-19: The Role of Particulate Matter in the Spread and Increase of COVID-19’s Morbidity and Mortality. Int. J. Environ. Res. Public Health, 2020, vol. 17, no. 12, pp. 4487. DOI: 10.3390/ijerph17124487
- Baumann R., Krzyzanowski M., Chicherin S. Framework plan for the development of monitoring of particulate matter in EECCA. Bonn, WHO European Centre for Environment and Health, 2006. Available at: https://www.euro.who.int/__data/assets/pdf_file/0019/130762/E88565.pdf (December 27, 2022).
- Kokoulin A.N., Kokoulin R.A. The Hierarchical Approach for Image Processing in Objects Recognition System. Pro-ceedings of the 2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), 2022, pp. 340–344.
- Wang W., Chang F. A Multi-focus Image Fusion Method Based on Laplacian Pyramid. Journal of Computers, 2011, vol. 6, no. 12, pp. 2559–2566. DOI: 10.4304/jcp.6.12.2559-2566
- Kokoulin A.N., Yuzhakov A.A., Kokoulin R.A. Multiscale Optical PM2.5 Particles Recognition and Sorting System in Dust Probes. 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech). Croatia, 2020, pp. 1–6. DOI: 10.23919/SpliTech49282.2020.9243759
- Lai W.-S., Huang J.-B., Ahuja N., Yang M.-H. Fast and Accurate Image Super-Resolution with Deep Laplacian Pyr-amid Networks. Available at: https://arxiv.org/abs/1710.01992 (February 10, 2023).
- Wronski B., Garcia-Dorado I., Ernst M., Kelly D., Krainin M., Liang C.-K., Levoy M., Milanfar P. Handheld Multi-Frame Super-Resolution. ACM Transactions on Graphics, vol. 38, no. 4, pp. 1–18. DOI: 10.1145/3306346.3323024
- Sysoeva E.V., Gel'manova M.O. Issledovanie zagryazneniya raiona Moskvy melkodispersnymi chastitsami pyli vblizi avtomobil'nykh dorog [Study of pollution of the Moscow region with fine dust particles near highways]. Aktual'nye problemy stroitel'noi otrasli i obrazovaniya: Sbornik dokladov Pervoi Natsional'noi konferentsii. Moscow, Natsional'nyi issledovatel'skii Moskovskii gosudarstvennyi stroitel'nyi universitet Publ., 2020, pp. 566–571 (in Russian).
- Volodina D.A., Talovskaya A.V., Yazikov E.G., Devyatova A.Yu., Edelev A.V. Assessment of dust and aerosol pollution in the zone of influence of the cement plant based on the study of snow cover (Novosibirsk region). Izvestiya Tomskogo politekhnicheskogo universiteta. Inzhiniring georesursov, 2022, vol. 333, no. 10, pp. 69–85. DOI: 10.18799/24131830/2022/10/3704 (in Russian).
- Budaeva Yu.S. Ecological and geochemical assessment of the territory of Yurga according to the data of studying the snow cover (Kemerovo region). Aktual'nye problemy nedropol'zovaniya: tezisy dokladov XVIII Mezhdunarodnogo foruma-konkursa studentov i molodykh uchenykh, Saint Petersburg, 2022, pp. 172–175 (in Russian).
- Methodology for monitoring dust concentrations in ambient air and analysis of collected measurements. CEE Bankwatch Network. Available at: https://bankwatch.org/wp-content/uploads/2020/07/Methodology-EDM-164.pdf (January 13, 2023).