Ruser, Heinrich; Schade, Julian; Jeong, Seongho; Mörtel, Max; Adelhardt, Mario; Adam, Thomas. (2024). Real-time particle analysis of explosives compounds using single-particle mass spectrometry. SPIE Next-Generation Spectroscopic Technologies XVI, Vol. 13026, 130260J-1 - 130260J-8.
Ruser, Heinrich; Schade, Julian; Jeong, Seongho; Mörtel, Max; Adelhardt, Mario; Adam, Thomas. (2024). Real-time particle analysis of explosives compounds using single-particle mass spectrometry. SPIE Next-Generation Spectroscopic Technologies XVI, Vol. 13026, 130260J-1 - 130260J-8.
Abstract
The detection of trace amounts of hazardous agents has become crucial for protection of human life, infrastructure and the environment. Single-particle mass spectrometry (SPMS) has proven to be a sensitive measurement technique for instantly revealing the chemical composition of individual, potentially harmful aerosol and dust particles. In this study, we focus on profiling non-volatile particles of explosive compounds in powdered form. It is reported, how the unique SPMS technology, based on (1) particle velocimetry and sizing, (2) sophisticated laser ionization and (3) bipolar time-of-flight mass spectrometry (TOF-MS) has been tailored and applied for the detection of individual particles of common explosive substances in the micro- and nanometer range. Out of more than 30 different types of military and home-made explosives, which were investigated in our recent laboratory measurements, the mass spectra of 12 commonly used explosives compounds are examined. Steps are described for automated and reliable identification of characteristic spectral markers of each of the explosives in their respective mass spectra.
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Anders, Lukas; Schade, Julian; Rosewig, Ellen Iva; Schmidt, Marco; Irsig, Robert; Jeong, Seongho; Käfer, Uwe; Gröger, Thomas; Bendl, Jan; Saraji-Bozorgzad, Mohammad Reza; Adam, Thomas; Etzien, Uwe; Czech, Hendryk; Buchholz, Bert; Streibel, Thorsten; Passig, Johannes; Zimmermann, Ralf. (2024). Polycyclic aromatic hydrocarbons as fuel-dependent markers in shi engine emissions using single-particle mass spectrometry. Environmental Science: Atmospheres, Advance Article.
Anders, Lukas; Schade, Julian; Rosewig, Ellen Iva; Schmidt, Marco; Irsig, Robert; Jeong, Seongho; Käfer, Uwe; Gröger, Thomas; Bendl, Jan; Saraji-Bozorgzad, Mohammad Reza; Adam, Thomas; Etzien, Uwe; Czech, Hendryk; Buchholz, Bert; Streibel, Thorsten; Passig, Johannes; Zimmermann, Ralf. (2024). Polycyclic aromatic hydrocarbons as fuel-dependent markers in shi engine emissions using single-particle mass spectrometry. Environmental Science: Atmospheres, Advance Article.
Abstract
We investigated the fuel-dependent single-particle mass spectrometric signatures of polycyclic aromatic hydrocarbons (PAHs) from the emissions of a research ship engine operating on marine gas oil (MGO), hydrotreated vegetable oil (HVO) and two heavy fuel oils (HFO), one with compliant and one with non-compliant fuel sulfur content. The PAH patterns are only slightly affected by the engine load and particle size, and contain sufficient dissimilarity to discriminate between the marine fuels used in our laboratory study. Hydrotreated vegetable oil (HVO) produced only weak PAH signals, supporting that fuel residues, rather than combustion conditions, determine the PAH emissions. The imprint of the fuel in the resulting PAH signatures, combined with novel single-particle characterization capabilities for inorganic and organic components, opens up new opportunities for source apportionment and air pollution monitoring. The approach is independent of metals, the traditional markers of ship emissions, which are becoming less important as new emission control policies are implemented and fuels become more diverse.
URL
https://pubs.rsc.org/en/content/articlelanding/2024/ea/d4ea00035h
Wang, Guanzhong; Ruser, Heinrich; Schade, Julian; Passig, Johannes; Zimmermann, Ralf; Dollinger, Günther; Adam, Thomas. (2024). Rapid Classification Of Aerosol Particle Mass Spectra Using Data Augmentation And Deep Learning. IEEE Conference on Artificial Intelligence, 1164-1169.
Wang, Guanzhong; Ruser, Heinrich; Schade, Julian; Passig, Johannes; Zimmermann, Ralf; Dollinger, Günther; Adam, Thomas. (2024). Rapid Classification Of Aerosol Particle Mass Spectra Using Data Augmentation And Deep Learning. IEEE Conference on Artificial Intelligence, 1164-1169.
Abstract
The concentration and chemical composition of airborne aerosol particles are important indicators of air quality and sources of air pollution. The particles’ chemical composition reveals probable emission sources, like traffic, biomass burning, wildfires, agriculture, or industrial sources. Single-particle mass spectrometry (SPMS), combined with rapid spectral classification, uniquely enables an in-situ analysis of the chemical composition of individual aerosol particles in real-time for environmental monitoring and other tasks. Modern SPMS devices analyze hundreds of individual particles per minute. Rapid and accurate classification of such large amounts of data remains challenging. Conventional clustering algorithms require tedious manual post-processing. A mass spectrum can be understood as a 1D image per analyzed particle. We applied CNN-based algorithms to perform a fully automated classification. To train the models, usually a large amount of labeled data needs to be prepared. With a manually created benchmark dataset containing 10,400 samples in 13 classes of emission sources (800 samples per class) we achieved an accuracy of ~90%. If the models are trained using only 100 labeled samples per class (1/8 labeled data), the models’ accuracy drops significantly to ~75%. We explored suitable augmentation methods to improve the reliability and performance of multi-class classification for aerosol particle mass spectra in case of limited labeled data (1/8 labeled data). The results using the augmented data improved from ~75% to 86.8%. This paves the way to sharply reduce the expensive and time-consuming work of expert labeling. Furthermore, we verified that converting the 1D mass spectrum into 2D representations and classifying them using 2D-CNN is more efficient than 1D-CNN networks, whether with or without data augmentation.
URL
https://ieeecai.org/2024/wp-content/pdfs/540900b164/540900b164.pdf
Rohkamp, Marius; Rabl, Alexander; Gündling, Benedikt; Saraji-Bozorgzad, Mohammad Reza; Mull, Christopher; Bendl, Jan; Neukirchen, Carsten; Helcig, Christian; Adam, Thomas; Gümmer, Volker; Hupfer, Andreas. (2024). Detailed Gaseous and Particulate Emissions of an Allison 250-C20B Turboshaft Engine. Journal of Engineering for Gas Turbines and Power, Vol. 146, No. 4.
Rohkamp, Marius; Rabl, Alexander; Gündling, Benedikt; Saraji-Bozorgzad, Mohammad Reza; Mull, Christopher; Bendl, Jan; Neukirchen, Carsten; Helcig, Christian; Adam, Thomas; Gümmer, Volker; Hupfer, Andreas. (2024). Detailed Gaseous and Particulate Emissions of an Allison 250-C20B Turboshaft Engine. Journal of Engineering for Gas Turbines and Power, Vol. 146, No. 4.
Abstract
Aviation is known to be one of the most significant contributors to air pollutants. This includes gaseous emissions, like carbon dioxide (CO2) and nitrogen oxides (NOx), and also particulate matter (PM), especially in the form of soot. This study conducted emission measurements on an Allison 250-C20B turboshaft engine operating on Jet A-1 fuel with a focus on gaseous compounds (e.g., ozone precursors) and PM. The different engine loading points were chosen based on the percentage thrust ratios of the International Civil Aviation Organization LTO-Cycle. A standard FTIR/O2/FID system to measure general gaseous combustion compounds, e.g., CO2, carbon monoxide (CO), unburned hydrocarbons (UHC), and NOx. For the investigation of the volatile organic compounds (VOC), which are known to act as ozone precursors, a gas chromatograph was applied. Different measurement methods were used to characterize the PM emissions. For the particle size distribution (PSD), we used two types of electrical mobility analyzers and an aerodynamic aerosol classifier. All measurement systems yielded comparable PSD results, indicating reliable results. The particle measurement methods all show increasing aerosol diameter modes (electrical and aerodynamic) with increased engine loading. The aerosol diameter modes were shifting from 29 nm to 65 nm. The size and shape of different individual particles were evaluated with a scanning electron microscope. A correlation between the injection system and the particle formation was established. Gaseous turboshaft engine emissions show high CO and UHC values at Ground Idle level. NOx levels were the highest at Take-Off conditions. Acetylene and ethylene were the most significant contributors to ozone formation.
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Neukirchen, Carsten; Meiners, Thorsten; Bendl, Jan; Zimmermann, Ralf; Adam, Thomas. (2024). Automated SEM/EDX imaging for the in-depth characterization of non-exhaust traffic emissions from the Munich subway system. Science of The Total Environment, Volume 915.
Neukirchen, Carsten; Meiners, Thorsten; Bendl, Jan; Zimmermann, Ralf; Adam, Thomas. (2024). Automated SEM/EDX imaging for the in-depth characterization of non-exhaust traffic emissions from the Munich subway system. Science of The Total Environment, Volume 915.
Abstract
A SEM/EDX based automated measurement and classification algorithm was tested as a method for the in-depth analysis of micro-environments in the Munich subway using a custom build mobile measurements system. Sampling was conducted at platform stations, to investigate the personal exposure of commuters to subway particulate matter during platform stays. EDX spectra and morphological features of all analyzed particles were automatically obtained and particles were automatically classified based on pre-defined chemical and morphological boundaries. Source apportionment for individual particles, such as abrasion processes at the wheel-brake interface, was partially possible based on the established particle classes. An average of 98.87 ± 1.06 % of over 200,000 analyzed particles were automatically assigned to the pre-defined classes, with 84.68 ± 16.45 % of particles classified as highly ferruginous. Manual EDX analysis further revealed, that heavy metal rich particles were also present in the ultrafine size range well below 100 nm.
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URL
https://www.sciencedirect.com/science/article/pii/S0048969724001426?via%3Dihub
Wang, G.; Ruser, H.; Schade, J.; Passig, J.; Zimmermann, R.; Dollinger, G.; Adam, T. (2024). CNN-Based Aerosol Particle Classification Using 2D Representations of Single-Particle Mass Spectrometer Data. 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (2024, Osaka).
Wang, G.; Ruser, H.; Schade, J.; Passig, J.; Zimmermann, R.; Dollinger, G.; Adam, T. (2024). CNN-Based Aerosol Particle Classification Using 2D Representations of Single-Particle Mass Spectrometer Data. 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (2024, Osaka).
Abstract
Single-particle mass spectrometry (SPMS) is a powerful real-time measurement technique to analyze the chemical composition of atmospheric aerosol particles: individual particles are desorbed and ionized to generate a bipolar mass spectrum that expresses the particle's chemical composition, giving clues to its origin and atmospheric processes. Popular approaches to classify SPMS data rely on clustering algorithms, resulting in the inability to achieve automated classification. Here, we present a modified deep learning approach for automatic classification of SPMS data in real-time. Before being processed by a convolutional neural network (CNN), the one-dimensional (1D) mass spectrum is converted into a two-dimensional (2D) representation, since in 2D, global and local features of the spectra are extracted more efficiently. Trained on real-world aerosol mass spectra from a month-long field measurement campaign, the proposed 2D-CNN model achieves a high mean classification accuracy of 92%, outperforming several well-known algorithms based on 2D-CNN, as well as a recently proposed 1D-CNN algorithm trained using 1D representations of mass spectra.
URL
Wang, G.; Ruser, H.; Schade, J.; Passig, J.; Adam, T.; Dollinger, G.; Zimmermann, R. (2024). Machine learning approaches for automatic classification of single-particle mass spectrometry data. Atmospheric Measurement Techniques, Vol. 17, S. 299–313.
Wang, G.; Ruser, H.; Schade, J.; Passig, J.; Adam, T.; Dollinger, G.; Zimmermann, R. (2024). Machine learning approaches for automatic classification of single-particle mass spectrometry data. Atmospheric Measurement Techniques, Vol. 17, S. 299–313.
Abstract
The chemical composition of aerosol particles is a key parameter for human health and climate effects. Single-particle mass spectrometry (SPMS) has evolved to a mature technology with unique chemical coverage and the capability to analyze the distribution of aerosol components in the particle ensemble in real time. With the fully automated characterization of the chemical profile of the aerosol particles, selective real-time monitoring of air quality could be performed, e.g., for urgent risk assessments due to particularly harmful pollutants. For aerosol particle classification, mostly unsupervised clustering algorithms (ART-2a, K-means and their derivatives) are used, which require manual postprocessing. In this work, we focus on supervised algorithms to tackle the problem of the automatic classification of large amounts of aerosol particle data. Supervised learning requires data with labels to train a predictive model. Therefore, we created a labeled benchmark dataset containing ∼ 24 000 particles with eight different coarse categories that were highly abundant at a measurement in summer in Central Europe: elemental carbon (EC), organic carbon and elemental carbon (OC-EC), potassium-rich (K-rich), calcium-rich (Ca-rich), iron-rich (Fe-rich), vanadium-rich (V-rich), magnesium-rich (Mg-rich) and sodium-rich (Na-rich). Using the chemical features of particles, the performance of the following classical supervised algorithms was tested: K-nearest neighbors, support vector machine, decision tree, random forest and multi-layer perceptron. This work shows that despite the entrenched position of unsupervised clustering algorithms in the field, the use of supervised algorithms has the potential to replace the manual step of clustering algorithms in many applications, where real-time data analysis is essential. For the classification of the eight classes, the prediction accuracy of several supervised algorithms exceeded 97 %. The trained model was used to classify ∼ 49 000 particles from a blind dataset in 0.2 s, taking into account also a class of “unclassified” particles. The predictions are highly consistent with the results obtained in a previous study using ART-2a.
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Streibel, T.; Jeong, S.; Bendl, J.; Saraji-Bozorgzad, M.; Sklorz, M.; Gehm, C.; Anders, L.; Passig, J.; Schade, J.; Etzien, U.; Adam, T.; Buchholz, B.; Schulz-Bull, D.E.; Zimmermann, R. (2024). Effects of Sulfur Scrubbers on Particulate Emissions from a Marine diesel engine. In: Proceedings of the 25th International Transport and Air Pollution (TAP) and the 3rd Shipping and Environment (SandE) Conference, Sjodin, Å., Moldanova, J., Laurelin, M., Cha, Y., Lundstrom, H. and Fontaras, G. (Eds.). S.288-291.
Streibel, T.; Jeong, S.; Bendl, J.; Saraji-Bozorgzad, M.; Sklorz, M.; Gehm, C.; Anders, L.; Passig, J.; Schade, J.; Etzien, U.; Adam, T.; Buchholz, B.; Schulz-Bull, D.E.; Zimmermann, R. (2024). Effects of Sulfur Scrubbers on Particulate Emissions from a Marine diesel engine. In: Proceedings of the 25th International Transport and Air Pollution (TAP) and the 3rd Shipping and Environment (SandE) Conference, Sjodin, Å., Moldanova, J., Laurelin, M., Cha, Y., Lundstrom, H. and Fontaras, G. (Eds.). S.288-291.
URL
https://publications.jrc.ec.europa.eu/repository/handle/JRC136825
Wang, G.; Ruser, H.; Schade, J.; Passig, J.; Adam, T.; Dollinger, G.; Zimmermann, R. (2024). In: Robustness Analysis for Classification of Aerosol Particles using Machine Learning with Two Different Single-Particle Mass Spectrometry Datasets. Proceedings of the 25th International Transport and Air Pollution (TAP) and the 3rd Shipping and Environment (SandE) Conference, Sjodin, Å., Moldanova, J., Laurelin, M., Cha, Y., Lundstrom, H. and Fontaras, G. (Eds.). S. 323-328.
Wang, G.; Ruser, H.; Schade, J.; Passig, J.; Adam, T.; Dollinger, G.; Zimmermann, R. (2024). In: Robustness Analysis for Classification of Aerosol Particles using Machine Learning with Two Different Single-Particle Mass Spectrometry Datasets. Proceedings of the 25th International Transport and Air Pollution (TAP) and the 3rd Shipping and Environment (SandE) Conference, Sjodin, Å., Moldanova, J., Laurelin, M., Cha, Y., Lundstrom, H. and Fontaras, G. (Eds.). S. 323-328.
Abstract
The chemical composition of aerosol particles in the air is successfully used to determine their origins, e.g. traffic emissions, biomass burning, or ship emissions. Single-Particle Mass Spectrometry (SPMS) is a sensitive measurement technique to analyze the chemical composition in real-time. The current mainstream classification methods in the SPMS community for handling these data require intensive manual post-processing, making an online analysis impossible. A few studies have demonstrated that supervised learning can perform automated classification of SPMS data with high accuracy, enabling selective air quality monitoring in real-time. However, the generalizability and reliability of those algorithms using SPMS data from different sources (e.g., different SPMS instruments, sampling locations, or weather conditions) are still key issues to be solved. This work investigates the classification generalization capacity (or robustness) of a multilayer perceptron network using two different datasets of SPMS data. The results show that the model trained on one dataset is sensitive to the disparate characteristic features of the other dataset, causing its prediction accuracy to decrease significantly. On the contrary, the model trained with data from both datasets performs strong robustness and adaptation to both datasets, with over 96 % correct classifications. The presented results underscore the feasibility and practicability of a uniform approach for automated profiling of data from different sources.
URL
https://publications.jrc.ec.europa.eu/repository/handle/JRC136825

















