Quantitative optical methods for advanced real-time characterization of ambient air particulate matter pollution (J2-2502)

General information

Title
Quantitative optical methods for advanced real-time characterization of ambient air particulate matter pollution
Period
Sep 1, 2020 -- Aug 31, 2023
Range
0.90 FTE
Activity
2.06 - Engineering Sciences and Technologies / Systems and Cybernetics

Abstract

The emissions caused by combustion of fuels for energy production, are a major cause of air pollution, and at the same time, due to their optical properties, also affect the climate change. Ambient air quality has a significant impact on human health, as it poses a risk that is difficult to avoid. In Europe, 90 percent of the urban population is exposed to excessive concentrations of particulate matter (PM), nitrogen oxides and ozone or benzene in the ambient air. Conservative estimates show that the number of premature deaths due to ambient air pollution per year in Europe is 790,000 and 7 million worldwide, and that the air pollution reduces the live expectancy in Europe by about 2.2 years, with an annual attributable mortality rate of 133/100,000. Data for Slovenia, obtained from the National Institute of Public Health of the Republic of Slovenia, show that 1500 people die every year due to air pollution. By reducing the level of PM in the air in excessively polluted areas, the life expectancy in Slovenia would increase by half to one year, which gives particular importance to the field of air pollution monitoring and management. With respect to particulates in ambient air, the restrictions usually apply to PM10 (particles with an aerodynamic diameter of less than 10 µm). EU directive 2008/50/EC establishes two limits related to PM10: the average daily concentration should not exceed 50 µg/m3 more than 35 times per year, and the average annual concentration should not exceed 40 µg/m3. However, since the toxicity of particles, which is not fully understood, is largely influenced by their chemical composition and size (the smallest particles penetrate the deepest into the lung, enter the blood stream), it is also necessary to measure the composition, mass and number densities of smaller PM2.5 and PM1 (aerodynamic diameter of less than 2.5 and 1 μm). Namely, the particle size distribution is log-normal, meaning that the number of particles smaller than 1 μm exceeds the number of particles larger than 1 μm but contributes very little to the total mass of PM10. Carbonaceous aerosols are a significant or largest fraction of PM2.5. Among carbonaceous aerosols, the most significant absorber of light is black carbon (BC) that was recognized by the latest Intergovernmental Panel on Climate Change report as the second most important contributor to global warming (immediately after CO2) with contributions ranging from 20% to 40%. Since aerosols also affect the global radiation balance by scattering of light, where their size and shape play a key role, we are not yet fully understanding the underlying processes. Many possibilities for high-impact research are open, in particular through developing novel methods for measuring the optical properties of aerosols that are directly related to the morphology and composition of particles and important for better understanding of climate change and of the impact on human health. The objective of the proposed research project is to develop new, innovative methods for measuring the optical properties of aerosols such as absorption and scattering coefficient, scattering phase function, and refractive index in real-time. A more detailed and complete information on the optical properties of aerosols will enable better classification of particles by composition, size and structure and at the same time enable better understanding of their impact on the human health and biological processes. Furthermore, knowledge of the optical properties of aerosols will also lead to a better understanding of the impact that aerosols have on the climate and its changes. By bringing together the two research groups with extensive experience and knowledge in design of state-of-the-art measurement systems for monitoring carbonaceous aerosols on one hand and utilization of light propagation modeling for estimation of optical properties on the other hand, the project creates a perfect environment for high impact research.

Phases of the project and their realization

Work Packages (WPs)
WP I
Development of novel light propagation and inverse models for quantitative assesment of optical properties and related physical quantities of PM in real-time
WP II
Collection of experimental data and validation of the light propagation models
WP III
Development of novel optical methods for characterization of PM in flow cells
Note
Due to the reduced funding of the project, some of the project tasks as outlined in the project proposal were not realized (marked as omitted).
2020/21
Development and initial validation of a parameterized Monte Carlo light propagation model that will allow improved quantitative analysis of PM deposited on filter substrate (WP I)
Completed
Development of ANN-based inverse models for estimation of optical properties of PM deposited on a filter substrate that can be used with different measurement settings (WP I)
Completed
Development and implementation of efficient Monte Carlo simulation framework for complex measurement settings that break down the symmetry of the detection scheme (WP I)
Completed
Evaluation and validation of inverse models for estimation of optical properties of PM deposited on filter substrate by simulated datasets (WP I)
Completed
Implementation of a computational framework for estimation of particle size and refractive index from angularly resolved reflectance measurements (WP III)
Omitted
Preparation of experimental settings for conducting reflectance/transmittance measurements using collimated transmittance, optical fiber probes, CMOS detector array and structured LCD/OLED illumination (WP II)
Completed
Dissemination of results in SCI journals and international conferences
Completed
2021/22
Preparation of experimental settings for laser-spackle, autofluorescence and hyperspectral imaging of PM samples deposited on filter substrate (WP II)
Completed
Measuring the optical properties of filter substrates (WP II)
Completed
Experimental validation of inverse models for estimation of optical properties of PM deposited on filer substrate using standard spherical particles with various diameters and refractive index (WP III)
Completed
Preparation of BC and mineral dust samples deposited on different filter substrates and collecting measurements with different measurement settings (WP II)
Omitted
Validation of the computational framework for estimation of particle size and refractive index from angularly resolved reflectance measurements (WP III)
Omitted
Preparation of experimental settings for lensless imaging and quantitative phase imaging of PM (WP III)
Omitted
Estimation of BC and mineral dust concentration from the optical properties derived by the inverse models. Use of optical properties for additional quantification of the morphology and composition of PM (WP II)
Omitted
Dissemination of results in SCI journals and international conferences
Completed
2022/23
Fusion and analysis of data collected by different optical techniques for enhanced quantification of PM deposited on filter substrate in terms of particle morphology and composition (WP II)
Omitted
Acquisition and analysis of data with lensless imaging using standard spherical particles with varying size and refractive index (WP III)
Omitted
Acquisition and analysis of data with quantitative phase imaging using standard spherical particles with varying size and refractive index (WP III)
Omitted
Dissemination of results in SCI journals and international conferences
Completed

Bibliographics records

1.
Miran Bürmen, Franjo Pernuš, Peter Naglič: MCDataset: a public reference dataset of Monte Carlo simulated quantities for multilayered and voxelated tissues computed by massively parallel PyXOpto Python package. Journal of Biomedical Optics, 27(8):083012, 2022 [COBISS-ID:110538243 ] [doi:10.1117/1.JBO.27.8.083012 ]
2.
Peter Naglič, Franjo Pernuš, Miran Bürmen: Reflectance calibration of multimode optical fiber probes by probe-to-target distance reflectance profile modeling. Measurement, 203:112002, 2022 [COBISS-ID:126338051 ] [doi:10.1016/j.measurement.2022.112002 ]
3.
Žan Cimperman, Peter Naglič, Franjo Pernuš, Boštjan Likar, Miran Bürmen: Autofocus algorithms for lensless on-chip microscopy validated on synthetic targets for microfluidic applications and particle tracking. SPIE Photonics Europe 2022: Unconventional Optical Imaging III, Apr 3-7, Strasbourg, France (M. P. Georges, G. Popescu, N. Verrier, Eds.), 12136:1213617, 2022 [COBISS-ID:110587395 ] [doi:10.1117/12.2621693 ]
4.
Yevhen Zelinskyi, Peter Naglič, Franjo Pernuš, Boštjan Likar, Miran Bürmen: Efficient Monte Carlo simulations of subdiffusive reflectance for spatial frequency domain imaging systems with a low NA. SPIE European Conference on Biomedical Optics 2021: Diffuse Optical Spectroscopy and Imaging VIII, Jun 20-24, Munich, Germany (D. Contini, Y. Hoshi, T. D. O'Sullivan, Eds.), 11920:119201U, 2021 [COBISS-ID:110616067 ] [doi:10.1117/12.2615422 ]
5.
Peter Naglič, Yevhen Zelinskyi, Franjo Pernuš, Boštjan Likar, Miran Bürmen: pyxopto: an open-source Python library with utilities for fast light propagation modeling in turbid media. SPIE European Conference on Biomedical Optics 2021: Diffuse Optical Spectroscopy and Imaging VIII, Jun 20-24, Munich, Germany (D. Contini, Y. Hoshi, T. D. O'Sullivan, Eds.), 11920:1192012, 2021 [COBISS-ID:110610179 ] [doi:10.1117/12.2615340 ]
6.
Miran Bürmen: Vrednotenje vpliva števila linearno razporejenih optičnih vlaken na kakovost napovedi optičnih lastnosti sipajočih vzorcev iz zajete reflektance [in Slovenian language]. Elektrotehniški vestnik - Electrotechnical Review, 88(1-2):41-48, 2021 [COBISS-ID:72247299 ]
7.
Ana Marin, Peter Naglič, Miran Bürmen: Open-source protocol for preparation of turbid phantoms with microsphere suspensions. SPIE Photonics West 2024: Design and Quality for Biomedical Technologies XVII, Jan 27-Feb 1, San Francisco, CA, USA (G. Vargas, Ed.), 12833:1283305, 2024 [COBISS-ID:190112003 ] [doi:10.1117/12.3001626 ]
8.
Ernesto Pini, Peter Naglič, Miran Bürmen, Alexander Gatto, Henrik Schäfer, Diederik Wiersma, Lorenzo Pattelli: Time-resolved light transport in structurally anisotropic media. SPIE Photonics West 2024: Biomedical Applications of Light Scattering XIV, Jan 27-Feb 1, San Francisco, CA, USA (A. Wax, V. Backman, Eds.), 12856:1285606, 2024 [COBISS-ID:190296835 ] [doi:10.1117/12.3002766 ]
9.
Peter Naglič, Ana Marin, Matic Ivančič, Martin Rigler, Miran Bürmen: Optical properties of particulate matter collected on glass fiber filters measured by spatially resolved reflectance spectroscopy. European Aerosol Conference - EAC2023, Sep 3-8, Malaga, Spain, 2023 [COBISS-ID:190230275 ]
10.
Peter Naglič, Ernesto Pini, Lorenzo Pattelli, Miran Bürmen: Massively parallel Monte Carlo simulations of light propagation in anisotropic scattering media by open-source PyXOpto engine. SPIE Photonics West 2024: Biomedical Applications of Light Scattering XIV, Jan 27-Feb 1, San Francisco, CA, USA (A. Wax, V. Backman, Eds.), 12856:1285608, 2024 [COBISS-ID:190648835 ] [doi:10.1117/12.3001838 ]
11.
Žan Cimperman: Numerical autofocusing methods for lensless holographic microscopy [in Slovenian language]. Master's thesis, University of Ljubljana, Faculty of Electrical Engineering (supervisor: Miran Bürmen, co-supervisor: Peter Naglič), 2022 [COBISS-ID:122288387 ]
12.
Miran Bürmen, Peter Naglič: Open-source Python framework for preparation of turbid phantoms with microsphere suspensions. GitHub, 2024 [COBISS-ID:191330563 ] [GitHub ]
13.
Miran Bürmen, Peter Naglič: Python-based library of scattering phase functions. GitHub, 2021 [COBISS-ID:72268803 ] [GitHub ]
14.
Miran Bürmen, Peter Naglič: Python-based OpenCL Monte Carlo light propagation model for layared media. GitHub, 2021 [COBISS-ID:72265219 ] [GitHub ]
15.
Miran Bürmen, Peter Naglič: Python-based OpenCL Monte Carlo light propagation model for voxelized media. GitHub, 2021 [COBISS-ID:72262659 ] [GitHub ]