Articles | Volume 17, issue 1
https://doi.org/10.5194/amt-17-299-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-299-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Jan 2024
Research article |
| 16 Jan 2024
| 16 Jan 2024
Machine learning approaches for automatic classification of single-particle mass spectrometry data
Guanzhong Wang
Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Julian Schade
Department of Mechanical Engineering, Institute of Chemistry and Environmental Engineering, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Johannes Passig
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Department of Life, Light and Matter, Faculty of Interdisciplinary Faculty, University of Rostock, 18059 Rostock, Germany
Thomas Adam
Department of Mechanical Engineering, Institute of Chemistry and Environmental Engineering, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Günther Dollinger
Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Ralf Zimmermann
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Department of Life, Light and Matter, Faculty of Interdisciplinary Faculty, University of Rostock, 18059 Rostock, Germany
Related authors
No articles found.
Marco Schmidt, Haseeb Hakkim, Lukas Anders, Aleksandrs Kalamašņikovs, Thomas Kröger-Badge, Robert Irsig, Norbert Graf, Reinhard Kelnberger, Johannes Passig, and Ralf Zimmermann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2587, https://doi.org/10.5194/egusphere-2024-2587, 2024
Short summary
Short summary
Laser desorption of individual particles prior to ionization is the key to reveal their organic composition. The CO2 lasers required are bulky and maintenance-intensive, limiting their use in the field. We have developed a compact solid-state IR laser that is easily aligned with the particle beam. Mass spectra and hit rates are similar to those of the CO2 laser. For combined characterization of organic and inorganic particle compositions both lasers are superior to conventional single UV pulses.
Satish Basnet, Anni Hartikainen, Aki Virkkula, Pasi Yli-Pirilä, Miika Kortelainen, Heikki Suhonen, Laura Kilpeläinen, Mika Ihalainen, Sampsa Väätäinen, Juho Louhisalmi, Markus Somero, Jarkko Tissari, Gert Jakobi, Ralf Zimmermann, Antti Kilpeläinen, and Olli Sippula
Atmos. Chem. Phys., 24, 3197–3215, https://doi.org/10.5194/acp-24-3197-2024, https://doi.org/10.5194/acp-24-3197-2024, 2024
Short summary
Short summary
Brown carbon (BrC) emissions were estimated, for residential wood combustion (RWC) from various northern European appliances, utilizing an extensive seven-wavelength aethalometer dataset and thermal–optical carbon analysis. The contribution of BrC370–950 to the absorption of visible light varied between 1 % and 21 %, and was linked with fuel moisture content and combustion efficiency. This study provides important information required for assessing the climate effects of RWC emissions.
Zhi-Hui Zhang, Elena Hartner, Battist Utinger, Benjamin Gfeller, Andreas Paul, Martin Sklorz, Hendryk Czech, Bin Xia Yang, Xin Yi Su, Gert Jakobi, Jürgen Orasche, Jürgen Schnelle-Kreis, Seongho Jeong, Thomas Gröger, Michal Pardo, Thorsten Hohaus, Thomas Adam, Astrid Kiendler-Scharr, Yinon Rudich, Ralf Zimmermann, and Markus Kalberer
Atmos. Chem. Phys., 22, 1793–1809, https://doi.org/10.5194/acp-22-1793-2022, https://doi.org/10.5194/acp-22-1793-2022, 2022
Short summary
Short summary
Using a novel setup, we comprehensively characterized the formation of particle-bound reactive oxygen species (ROS) in anthropogenic and biogenic secondary organic aerosols (SOAs). We found that more than 90 % of all ROS components in both SOA types have a short lifetime. Our results also show that photochemical aging promotes particle-bound ROS production and enhances the oxidative potential of the aerosols. We found consistent results between chemical-based and biological-based ROS analyses.
Johannes Passig, Julian Schade, Robert Irsig, Thomas Kröger-Badge, Hendryk Czech, Thomas Adam, Henrik Fallgren, Jana Moldanova, Martin Sklorz, Thorsten Streibel, and Ralf Zimmermann
Atmos. Chem. Phys., 22, 1495–1514, https://doi.org/10.5194/acp-22-1495-2022, https://doi.org/10.5194/acp-22-1495-2022, 2022
Short summary
Short summary
The single-particle distribution of health-relevant polycyclic aromatic hydrocarbons (PAHs) was studied at the Swedish coast in autumn. We found PAHs bound to long-range transported particles from eastern and central Europe and also from ship emissions and local sources. This is the first field study using a new technology revealing single-particle data from both inorganic components and PAHs. We discuss PAH profiles that are indicative of several sources and atmospheric aging processes.
Xiansheng Liu, Hadiatullah Hadiatullah, Xun Zhang, L. Drew Hill, Andrew H. A. White, Jürgen Schnelle-Kreis, Jan Bendl, Gert Jakobi, Brigitte Schloter-Hai, and Ralf Zimmermann
Atmos. Meas. Tech., 14, 5139–5151, https://doi.org/10.5194/amt-14-5139-2021, https://doi.org/10.5194/amt-14-5139-2021, 2021
Short summary
Short summary
A monitoring campaign was conducted in Augsburg to determine a suitable noise reduction algorithm for the MA200 Aethalometer. Results showed that centred moving average (CMA) post-processing effectively removed spurious negative concentrations without major bias and reliably highlighted effects from local sources, effectively increasing spatio-temporal resolution in mobile measurements. Evaluation of each method on peak sample reduction and background correction further supports the reliability.
Johannes Passig, Julian Schade, Robert Irsig, Lei Li, Xue Li, Zhen Zhou, Thomas Adam, and Ralf Zimmermann
Atmos. Meas. Tech., 14, 4171–4185, https://doi.org/10.5194/amt-14-4171-2021, https://doi.org/10.5194/amt-14-4171-2021, 2021
Short summary
Short summary
Ships are major sources of air pollution; however, monitoring of ship emissions outside harbours is a challenging task. We optimized single-particle mass spectrometry (SPMS) for the detection of bunker fuel emissions and demonstrate the detection of individual ship plumes from more than 10 km in distance. The approach works independently of background air pollution and also when ships use exhaust-cleaning scrubbers. We discuss the potential and limits of SPMS-based monitoring of ship plumes.
Dac-Loc Nguyen, Hendryk Czech, Simone M. Pieber, Jürgen Schnelle-Kreis, Martin Steinbacher, Jürgen Orasche, Stephan Henne, Olga B. Popovicheva, Gülcin Abbaszade, Guenter Engling, Nicolas Bukowiecki, Nhat-Anh Nguyen, Xuan-Anh Nguyen, and Ralf Zimmermann
Atmos. Chem. Phys., 21, 8293–8312, https://doi.org/10.5194/acp-21-8293-2021, https://doi.org/10.5194/acp-21-8293-2021, 2021
Short summary
Short summary
Southeast Asia is well-known for emission-intense and recurring wildfires and after-harvest crop residue burning during the pre-monsoon season from February to April. We describe a biomass burning (BB) plume arriving at remote Pha Din meteorological station, outline its carbonaceous particulate matter (PM) constituents based on more than 50 target compounds and discuss possible BB sources. This study adds valuable information on chemical PM composition for a region with scarce data availability.
Johannes Passig, Julian Schade, Ellen Iva Rosewig, Robert Irsig, Thomas Kröger-Badge, Hendryk Czech, Martin Sklorz, Thorsten Streibel, Lei Li, Xue Li, Zhen Zhou, Henrik Fallgren, Jana Moldanova, and Ralf Zimmermann
Atmos. Chem. Phys., 20, 7139–7152, https://doi.org/10.5194/acp-20-7139-2020, https://doi.org/10.5194/acp-20-7139-2020, 2020
Short summary
Short summary
Particle-bound metals in both natural dusts and polluted air can induce severe health effects. They are also transported by the wind into the oceans; provide micronutrients; and thus modulate biodiversity, fisheries, and climate. We show a way to more efficiently detect metals in individual particles while preserving source information. Our detection scheme is less dependent on the particle type and atmospheric changes and is thus valuable to the study of biogechemical cycles and air pollution.
Anni Hartikainen, Petri Tiitta, Mika Ihalainen, Pasi Yli-Pirilä, Jürgen Orasche, Hendryk Czech, Miika Kortelainen, Heikki Lamberg, Heikki Suhonen, Hanna Koponen, Liqing Hao, Ralf Zimmermann, Jorma Jokiniemi, Jarkko Tissari, and Olli Sippula
Atmos. Chem. Phys., 20, 6357–6378, https://doi.org/10.5194/acp-20-6357-2020, https://doi.org/10.5194/acp-20-6357-2020, 2020
Short summary
Short summary
Residential wood combustion emits large amounts of organic compounds, which are transformed in the atmosphere via photochemical ageing reactions. We assessed this organic emission at various stages of exposure with an oxidation flow reactor. Ageing led to major changes in both gaseous and particulate phases including increased acidic compounds and transformation of the polycyclic aromatic compounds. Such changes have serious implications for the health- and climate-related effects of combustion.
Chunlin Li, Quanfu He, Julian Schade, Johannes Passig, Ralf Zimmermann, Daphne Meidan, Alexander Laskin, and Yinon Rudich
Atmos. Chem. Phys., 19, 139–163, https://doi.org/10.5194/acp-19-139-2019, https://doi.org/10.5194/acp-19-139-2019, 2019
Petri Tiitta, Ari Leskinen, Liqing Hao, Pasi Yli-Pirilä, Miika Kortelainen, Julija Grigonyte, Jarkko Tissari, Heikki Lamberg, Anni Hartikainen, Kari Kuuspalo, Aki-Matti Kortelainen, Annele Virtanen, Kari E. J. Lehtinen, Mika Komppula, Simone Pieber, André S. H. Prévôt, Timothy B. Onasch, Douglas R. Worsnop, Hendryk Czech, Ralf Zimmermann, Jorma Jokiniemi, and Olli Sippula
Atmos. Chem. Phys., 16, 13251–13269, https://doi.org/10.5194/acp-16-13251-2016, https://doi.org/10.5194/acp-16-13251-2016, 2016
Short summary
Short summary
Real-time measurements of OA aging and SOA formation from logwood combustion were conducted under dark and UV oxidation. Substantial SOA formation was observed in all experiments, leading to twice the initial OA mass emphasizing the importance of the burning conditions for the aging processes. The results prove that emissions are subject to intensive chemical processing in the atmosphere; e.g. the most of the POA was found to become oxidized after the ozone addition, forming aged POA.
Related subject area
Subject: Aerosols | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Spatial analysis of PM2.5 using a concentration similarity index applied to air quality sensor networks
A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization
Towards a hygroscopic growth calibration for low-cost PM2.5 sensors
Enhancing characterization of organic nitrogen components in aerosols and droplets using high-resolution aerosol mass spectrometry
A searchable database and mass spectral comparison tool for the Aerosol Mass Spectrometer (AMS) and the Aerosol Chemical Speciation Monitor (ACSM)
Numerical investigation on retrieval errors of mixing states of fractal black carbon aerosols using single-particle soot photometer based on Mie scattering and the effects on radiative forcing estimation
Performance evaluation of MOMA (MOment MAtching) – a remote network calibration technique for PM2.5 and PM10 sensors
Mapping the performance of a versatile water-based condensation particle counter (vWCPC) with numerical simulation and experimental study
Development and evaluation of an improved offline aerosol mass spectrometry technique
SMEARcore – modular data infrastructure for atmospheric measurement stations
A multiple-charging correction algorithm for a broad-supersaturation scanning cloud condensation nuclei (BS2-CCN) system
An evaluation of the U.S. EPA's correction equation for PurpleAir sensor data in smoke, dust, and wintertime urban pollution events
Typhoon-associated air quality over the Guangdong–Hong Kong–Macao Greater Bay Area, China: machine-learning-based prediction and assessment
Quantification of primary and secondary organic aerosol sources by combined factor analysis of extractive electrospray ionisation and aerosol mass spectrometer measurements (EESI-TOF and AMS)
A new method for calculating average visibility from the relationship between extinction coefficient and visibility
In situ particle sampling relationships to surface and turbulent fluxes using large eddy simulations with Lagrangian particles
The effect of the averaging period for PMF analysis of aerosol mass spectrometer measurements during offline applications
Calibrating networks of low-cost air quality sensors
Source apportionment resolved by time of day for improved deconvolution of primary source contributions to air pollution
Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data
Rolling vs. seasonal PMF: real-world multi-site and synthetic dataset comparison
Comprehensive detection of analytes in large chromatographic datasets by coupling factor analysis with a decision tree
Combined organic and inorganic source apportionment on yearlong ToF-ACSM dataset at a suburban station in Athens
Retrieval of the sea spray aerosol mode from submicron particle size distributions and supermicron scattering during LASIC
Automated identification of local contamination in remote atmospheric composition time series
Ch3MS-RF: a random forest model for chemical characterization and improved quantification of unidentified atmospheric organics detected by chromatography–mass spectrometry techniques
Regularized inversion of aerosol hygroscopic growth factor probability density function: application to humidity-controlled fast integrated mobility spectrometer measurements
A systematic re-evaluation of methods for quantification of bulk particle-phase organic nitrates using real-time aerosol mass spectrometry
Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data
Data imputation in in situ-measured particle size distributions by means of neural networks
Analysis of mobile monitoring data from the microAeth® MA200 for measuring changes in black carbon on the roadside in Augsburg
New correction method for the scattering coefficient measurements of a three-wavelength nephelometer
Estimating mean molecular weight, carbon number, and OM∕OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network
Modeled source apportionment of black carbon particles coated with a light-scattering shell
Estimation of particulate organic nitrates from thermodenuder–aerosol mass spectrometer measurements in the North China Plain
Aerosol pH indicator and organosulfate detectability from aerosol mass spectrometry measurements
Determination of equivalent black carbon mass concentration from aerosol light absorption using variable mass absorption cross section
Effects of multi-charge on aerosol hygroscopicity measurement by a HTDMA
A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data
Estimation of pollen counts from light scattering intensity when sampling multiple pollen taxa – establishment of an automated multi-taxa pollen counting estimation system (AME system)
A novel lidar gradient cluster analysis method of nocturnal boundary layer detection during air pollution episodes
Assessment of particle size magnifier inversion methods to obtain the particle size distribution from atmospheric measurements
A global analysis of climate-relevant aerosol properties retrieved from the network of Global Atmosphere Watch (GAW) near-surface observatories
Development of an automatic linear calibration method for high-resolution single-particle mass spectrometry: improved chemical species identification for atmospheric aerosols
A hybrid method for reconstructing the historical evolution of aerosol optical depth from sunshine duration measurements
The influence of the baseline drift on the resulting extinction values of a cavity attenuated phase shift-based extinction monitor (CAPS PMex)
Evaluation of equivalent black carbon source apportionment using observations from Switzerland between 2008 and 2018
Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations
Filling the gaps of in situ hourly PM2.5 concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles
Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi
Rósín Byrne, John C. Wenger, and Stig Hellebust
Atmos. Meas. Tech., 17, 5129–5146, https://doi.org/10.5194/amt-17-5129-2024, https://doi.org/10.5194/amt-17-5129-2024, 2024
Short summary
Short summary
This study presents the concentration similarity index (CSI) for a quantitative and robust comparison of PM2.5 measurements within air quality sensor networks. Developed and tested on two Irish sensor networks, the CSI revealed real spatial variations in PM2.5 and enables assessment of the representativeness of regulatory monitoring locations. It underscores the impact of solid fuel combustion on PM2.5 and highlights the importance of wintertime data for accurate exposure assessments.
Anton Rusanen, Anton Björklund, Manousos I. Manousakas, Jianhui Jiang, Markku T. Kulmala, Kai Puolamäki, and Kaspar R. Daellenbach
Atmos. Meas. Tech., 17, 1251–1277, https://doi.org/10.5194/amt-17-1251-2024, https://doi.org/10.5194/amt-17-1251-2024, 2024
Short summary
Short summary
We present a Bayesian non-negative matrix factorization model that performs better on our test datasets than currently widely used models. Its advantages are better use of time information and providing a direct error estimation. We believe this could lead to better estimates of emission sources from measurements.
Milan Y. Patel, Pietro F. Vannucci, Jinsol Kim, William M. Berelson, and Ronald C. Cohen
Atmos. Meas. Tech., 17, 1051–1060, https://doi.org/10.5194/amt-17-1051-2024, https://doi.org/10.5194/amt-17-1051-2024, 2024
Short summary
Short summary
Low-cost particulate matter (PM) sensors are becoming increasingly common in community monitoring and atmospheric research, but these sensors require proper calibration to provide accurate reporting. Here, we propose a hygroscopic growth calibration scheme that evolves in time to account for seasonal changes in hygroscopic growth. In San Francisco and Los Angeles, CA, applying a seasonal hygroscopic growth calibration can account for sensor biases driven by the seasonal cycles in PM composition.
Xinlei Ge, Yele Sun, Justin Trousdell, Mindong Chen, and Qi Zhang
Atmos. Meas. Tech., 17, 423–439, https://doi.org/10.5194/amt-17-423-2024, https://doi.org/10.5194/amt-17-423-2024, 2024
Short summary
Short summary
This study aims to enhance the application of the Aerodyne high-resolution aerosol mass spectrometer (HR-AMS) in characterizing organic nitrogen (ON) species within aerosol particles and droplets. A thorough analysis was conducted on 75 ON standards that represent a diverse spectrum of ambient ON types. The results underscore the capacity of the HR-AMS in examining the concentration and chemistry of atmospheric ON compounds, thereby offering insights into their sources and environmental impacts.
Sohyeon Jeon, Michael J. Walker, Donna T. Sueper, Douglas A. Day, Anne V. Handschy, Jose L. Jimenez, and Brent J. Williams
Atmos. Meas. Tech., 16, 6075–6095, https://doi.org/10.5194/amt-16-6075-2023, https://doi.org/10.5194/amt-16-6075-2023, 2023
Short summary
Short summary
A searchable database tool for the Aerosol Mass Spectrometer (AMS) and Aerosol Chemical Speciation Monitor (ACSM) mass spectral datasets was built to improve the efficiency of data analysis using Igor Pro. The tool incorporates the published mass spectra (MS) and sample information uploaded on the website. The tool allows users to compare their own mass spectrum with the reference MS in the database.
Jia Liu, Guangya Wang, Cancan Zhu, Donghui Zhou, and Lin Wang
Atmos. Meas. Tech., 16, 4961–4974, https://doi.org/10.5194/amt-16-4961-2023, https://doi.org/10.5194/amt-16-4961-2023, 2023
Short summary
Short summary
Single-particle soot photometer (SP2) employs the core-shell model to represent coated BC particles, which introduces retrieval errors in the mixing state (Dp/Dc) of BC. We construct fractal models to represent thinly and thickly coated BC particles, and the retrieval errors of the mixing state are investigated from the numerical aspect. We find that errors in Dp/Dc are noteworthy, and the errors in Dp/Dc can further affect the evaluation accuracy of the radiative forcing of BC.
Lena Francesca Weissert, Geoff Steven Henshaw, David Edward Williams, Brandon Feenstra, Randy Lam, Ashley Collier-Oxandale, Vasileios Papapostolou, and Andrea Polidori
Atmos. Meas. Tech., 16, 4709–4722, https://doi.org/10.5194/amt-16-4709-2023, https://doi.org/10.5194/amt-16-4709-2023, 2023
Short summary
Short summary
We apply a previously developed remote calibration framework to a network of particulate matter (PM) sensors deployed in Southern California. Our results show that a remote calibration can improve the accuracy of PM data, which was particularly visible for PM10. We highlight that sensor drift was mostly due to differences in particle composition than monitor operational factors. Thus, PM sensors may require frequent calibration if PM sources vary with different wind conditions or seasons.
Weixing Hao, Fan Mei, Susanne Hering, Steven Spielman, Beat Schmid, Jason Tomlinson, and Yang Wang
Atmos. Meas. Tech., 16, 3973–3986, https://doi.org/10.5194/amt-16-3973-2023, https://doi.org/10.5194/amt-16-3973-2023, 2023
Short summary
Short summary
Airborne aerosol instrumentation plays a crucial role in understanding the spatial distribution of ambient aerosol particles. This study investigates a versatile water-based condensation particle counter through simulations and experiments. It provides valuable insights to improve versatile water-based condensation particle counter (vWCPC) aerosol measurement and operation for the community.
Christina N. Vasilakopoulou, Kalliopi Florou, Christos Kaltsonoudis, Iasonas Stavroulas, Nikolaos Mihalopoulos, and Spyros N. Pandis
Atmos. Meas. Tech., 16, 2837–2850, https://doi.org/10.5194/amt-16-2837-2023, https://doi.org/10.5194/amt-16-2837-2023, 2023
Short summary
Short summary
The offline aerosol mass spectrometry technique is a useful tool for the source apportionment of organic aerosol in areas and periods during which an aerosol mass spectrometer is not available. In this work, an improved offline technique was developed and evaluated in an effort to capture most of the partially soluble and insoluble organic aerosol material, reducing the uncertainty of the corresponding source apportionment significantly.
Anton Rusanen, Kristo Hõrrak, Lauri R. Ahonen, Tuomo Nieminen, Pasi P. Aalto, Pasi Kolari, Markku Kulmala, Tuukka Petäjä, and Heikki Junninen
Atmos. Meas. Tech., 16, 2781–2793, https://doi.org/10.5194/amt-16-2781-2023, https://doi.org/10.5194/amt-16-2781-2023, 2023
Short summary
Short summary
We present a framework for setting up SMEAR (Station for Measuring Ecosystem–Atmosphere Relations) type measurement station data flows. This framework, called SMEARcore, consists of modular open-source software components that can be chosen to suit various station configurations. The benefits of using this framework are automation of routine operations and real-time monitoring of measurement results.
Najin Kim, Hang Su, Nan Ma, Ulrich Pöschl, and Yafang Cheng
Atmos. Meas. Tech., 16, 2771–2780, https://doi.org/10.5194/amt-16-2771-2023, https://doi.org/10.5194/amt-16-2771-2023, 2023
Short summary
Short summary
We propose a multiple-charging correction algorithm for a broad-supersaturation scanning cloud condensation nuclei (BS2-CCN) system which can obtain high time-resolution aerosol hygroscopicity and CCN activity. The correction algorithm aims at deriving the activation fraction's true value for each particle size. The meaningful differences between corrected and original κ values (single hygroscopicity parameter) emphasize the correction algorithm's importance for ambient aerosol measurement.
Daniel A. Jaffe, Colleen Miller, Katie Thompson, Brandon Finley, Manna Nelson, James Ouimette, and Elisabeth Andrews
Atmos. Meas. Tech., 16, 1311–1322, https://doi.org/10.5194/amt-16-1311-2023, https://doi.org/10.5194/amt-16-1311-2023, 2023
Short summary
Short summary
PurpleAir sensors (PASs) are low-cost tools to measure fine particulate matter (PM) concentrations. However, the raw PAS data have significant biases, so the sensors must be corrected. We analyzed data from numerous sites and found that the standard correction to the PAS Purple Air data is accurate in urban pollution events and smoke events but leads to a 6-fold underestimate in the PM2.5 concentrations in dust events. We propose a new correction algorithm to address this problem.
Yilin Chen, Yuanjian Yang, and Meng Gao
Atmos. Meas. Tech., 16, 1279–1294, https://doi.org/10.5194/amt-16-1279-2023, https://doi.org/10.5194/amt-16-1279-2023, 2023
Short summary
Short summary
The Guangdong–Hong Kong–Macao Greater Bay Area suffers from summertime air pollution events related to typhoons. The present study leverages machine learning to predict typhoon-associated air quality over the area. The model evaluation shows that the model performs excellently. Moreover, the change in meteorological drivers of air quality on typhoon days and non-typhoon days suggests that air pollution control strategies should have different focuses on typhoon days and non-typhoon days.
Yandong Tong, Lu Qi, Giulia Stefenelli, Dongyu Simon Wang, Francesco Canonaco, Urs Baltensperger, André Stephan Henry Prévôt, and Jay Gates Slowik
Atmos. Meas. Tech., 15, 7265–7291, https://doi.org/10.5194/amt-15-7265-2022, https://doi.org/10.5194/amt-15-7265-2022, 2022
Short summary
Short summary
We present a method for positive matrix factorisation (PMF) analysis on a single dataset that includes measurements from both EESI-TOF and AMS in Zurich, Switzerland. For the first time, we resolved and quantified secondary organic aerosol (SOA) sources. Meanwhile, we also determined the retrieved EESI-TOF factor-dependent sensitivities. This method provides a framework for exploiting semi-quantitative, high-resolution instrumentation for quantitative source apportionment.
Zefeng Zhang, Hengnan Guo, Hanqing Kang, Jing Wang, Junlin An, Xingna Yu, Jingjing Lv, and Bin Zhu
Atmos. Meas. Tech., 15, 7259–7264, https://doi.org/10.5194/amt-15-7259-2022, https://doi.org/10.5194/amt-15-7259-2022, 2022
Short summary
Short summary
In this study, we first analyze the relationship between the visibility, the extinction coefficient, and atmospheric compositions. Then we propose to use the harmonic average of visibility data as the average visibility, which can better reflect changes in atmospheric extinction coefficients and aerosol concentrations. It is recommended to use the harmonic average visibility in the studies of climate change, atmospheric radiation, air pollution, environmental health, etc.
Hyungwon John Park, Jeffrey S. Reid, Livia S. Freire, Christopher Jackson, and David H. Richter
Atmos. Meas. Tech., 15, 7171–7194, https://doi.org/10.5194/amt-15-7171-2022, https://doi.org/10.5194/amt-15-7171-2022, 2022
Short summary
Short summary
We use numerical models to study field measurements of sea spray aerosol particles and conclude that both the atmospheric state and the methods of instrument sampling are causes for the variation in the production rate of aerosol particles: a critical metric to learn the aerosol's effect on processes like cloud physics and radiation. This work helps field observers improve their experimental design and interpretation of measurements because of turbulence in the atmosphere.
Christina Vasilakopoulou, Iasonas Stavroulas, Nikolaos Mihalopoulos, and Spyros N. Pandis
Atmos. Meas. Tech., 15, 6419–6431, https://doi.org/10.5194/amt-15-6419-2022, https://doi.org/10.5194/amt-15-6419-2022, 2022
Short summary
Short summary
Offline aerosol mass spectrometer (AMS) measurements can provide valuable information about ambient organic aerosols when online AMS measurements are not available. In this study, we examine whether and how the low time resolution (usually 24 h) of the offline technique affects source apportionment results. We concluded that use of the daily averages resulted in estimated average contributions that were within 8 % of the total OA compared with the high-resolution analysis.
Priyanka deSouza, Ralph Kahn, Tehya Stockman, William Obermann, Ben Crawford, An Wang, James Crooks, Jing Li, and Patrick Kinney
Atmos. Meas. Tech., 15, 6309–6328, https://doi.org/10.5194/amt-15-6309-2022, https://doi.org/10.5194/amt-15-6309-2022, 2022
Short summary
Short summary
How sensitive are the spatial and temporal trends of PM2.5 derived from a network of low-cost sensors to the calibration adjustment used? How transferable are calibration equations developed at a few co-location sites to an entire network of low-cost sensors? This paper attempts to answer this question and offers a series of suggestions on how to develop the most robust calibration function for different end uses. It uses measurements from the Love My Air network in Denver as a test case.
Sahil Bhandari, Zainab Arub, Gazala Habib, Joshua S. Apte, and Lea Hildebrandt Ruiz
Atmos. Meas. Tech., 15, 6051–6074, https://doi.org/10.5194/amt-15-6051-2022, https://doi.org/10.5194/amt-15-6051-2022, 2022
Short summary
Short summary
We present a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization. We report results for four example time periods in two seasons (winter and monsoon 2017) in Delhi, India. Compared to the traditional approach, we extract a larger number of factors that represent the expected sources of primary organic aerosol. This method can capture diurnal time series patterns of sources at low computational cost.
Alireza Moallemi, Rob L. Modini, Tatyana Lapyonok, Anton Lopatin, David Fuertes, Oleg Dubovik, Philippe Giaccari, and Martin Gysel-Beer
Atmos. Meas. Tech., 15, 5619–5642, https://doi.org/10.5194/amt-15-5619-2022, https://doi.org/10.5194/amt-15-5619-2022, 2022
Short summary
Short summary
Aerosol properties (size distributions, refractive indices) can be retrieved from in situ, angularly resolved light scattering measurements performed with polar nephelometers. We apply an established framework to assess the aerosol property retrieval potential for different instrument configurations, target applications, and assumed prior knowledge. We also demonstrate how a reductive greedy algorithm can be used to determine the optimal placements of the angular sensors in a polar nephelometer.
Marta Via, Gang Chen, Francesco Canonaco, Kaspar R. Daellenbach, Benjamin Chazeau, Hasna Chebaicheb, Jianhui Jiang, Hannes Keernik, Chunshui Lin, Nicolas Marchand, Cristina Marin, Colin O'Dowd, Jurgita Ovadnevaite, Jean-Eudes Petit, Michael Pikridas, Véronique Riffault, Jean Sciare, Jay G. Slowik, Leïla Simon, Jeni Vasilescu, Yunjiang Zhang, Olivier Favez, André S. H. Prévôt, Andrés Alastuey, and María Cruz Minguillón
Atmos. Meas. Tech., 15, 5479–5495, https://doi.org/10.5194/amt-15-5479-2022, https://doi.org/10.5194/amt-15-5479-2022, 2022
Short summary
Short summary
This work presents the differences resulting from two techniques (rolling and seasonal) of the positive matrix factorisation model that can be run for organic aerosol source apportionment. The current state of the art suggests that the rolling technique is more accurate, but no proof of its effectiveness has been provided yet. This paper tackles this issue in the context of a synthetic dataset and a multi-site real-world comparison.
Sungwoo Kim, Brian M. Lerner, Donna T. Sueper, and Gabriel Isaacman-VanWertz
Atmos. Meas. Tech., 15, 5061–5075, https://doi.org/10.5194/amt-15-5061-2022, https://doi.org/10.5194/amt-15-5061-2022, 2022
Short summary
Short summary
Atmospheric samples can be complex, and current analysis methods often require substantial human interaction and discard potentially important information. To improve analysis accuracy and computational cost of these large datasets, we developed an automated analysis algorithm that utilizes a factor analysis approach coupled with a decision tree. We demonstrate that this algorithm cataloged approximately 10 times more analytes compared to a manual analysis and in a quarter of the analysis time.
Olga Zografou, Maria Gini, Manousos I. Manousakas, Gang Chen, Athina C. Kalogridis, Evangelia Diapouli, Athina Pappa, and Konstantinos Eleftheriadis
Atmos. Meas. Tech., 15, 4675–4692, https://doi.org/10.5194/amt-15-4675-2022, https://doi.org/10.5194/amt-15-4675-2022, 2022
Short summary
Short summary
A yearlong ToF-ACSM dataset was used to characterize ambient aerosols over a suburban Athenian site, and innovative software for source apportionment was implemented in order to distinguish the sources of the total non-refractory species of PM1. A comparison between the methodology of combined organic and inorganic PMF analysis and the conventional organic PMF took place.
Jeramy L. Dedrick, Georges Saliba, Abigail S. Williams, Lynn M. Russell, and Dan Lubin
Atmos. Meas. Tech., 15, 4171–4194, https://doi.org/10.5194/amt-15-4171-2022, https://doi.org/10.5194/amt-15-4171-2022, 2022
Short summary
Short summary
A new method is presented to retrieve the sea spray aerosol size distribution by combining submicron size and nephelometer scattering based on Mie theory. Using available sea spray tracers, we find that this approach serves as a comparable substitute to supermicron size distribution measurements, which are limited in availability at marine sites. Application of this technique can expand sea spray observations and improve the characterization of marine aerosol impacts on clouds and climate.
Ivo Beck, Hélène Angot, Andrea Baccarini, Lubna Dada, Lauriane Quéléver, Tuija Jokinen, Tiia Laurila, Markus Lampimäki, Nicolas Bukowiecki, Matthew Boyer, Xianda Gong, Martin Gysel-Beer, Tuukka Petäjä, Jian Wang, and Julia Schmale
Atmos. Meas. Tech., 15, 4195–4224, https://doi.org/10.5194/amt-15-4195-2022, https://doi.org/10.5194/amt-15-4195-2022, 2022
Short summary
Short summary
We present the pollution detection algorithm (PDA), a new method to identify local primary pollution in remote atmospheric aerosol and trace gas time series. The PDA identifies periods of contaminated data and relies only on the target dataset itself; i.e., it is independent of ancillary data such as meteorological variables. The parameters of all pollution identification steps are adjustable so that the PDA can be tuned to different locations and situations. It is available as open-access code.
Emily B. Franklin, Lindsay D. Yee, Bernard Aumont, Robert J. Weber, Paul Grigas, and Allen H. Goldstein
Atmos. Meas. Tech., 15, 3779–3803, https://doi.org/10.5194/amt-15-3779-2022, https://doi.org/10.5194/amt-15-3779-2022, 2022
Short summary
Short summary
The composition of atmospheric aerosols are extremely complex, containing hundreds of thousands of estimated individual compounds. The majority of these compounds have never been catalogued in widely used databases, making them extremely difficult for atmospheric chemists to identify and analyze. In this work, we present Ch3MS-RF, a machine-learning-based model to enable characterization of complex mixtures and prediction of structure-specific properties of unidentifiable organic compounds.
Jiaoshi Zhang, Yang Wang, Steven Spielman, Susanne Hering, and Jian Wang
Atmos. Meas. Tech., 15, 2579–2590, https://doi.org/10.5194/amt-15-2579-2022, https://doi.org/10.5194/amt-15-2579-2022, 2022
Short summary
Short summary
New nonparametric, regularized methods are developed to invert the growth factor probability density function (GF-PDF) from humidity-controlled fast integrated mobility spectrometer measurements. These algorithms are computationally efficient, require no prior assumptions of the GF-PDF distribution, and reduce the error in inverted GF-PDF. They can be applied to humidified tandem differential mobility analyzer data. Among all algorithms, Twomey’s method retrieves GF-PDF with the smallest error.
Douglas A. Day, Pedro Campuzano-Jost, Benjamin A. Nault, Brett B. Palm, Weiwei Hu, Hongyu Guo, Paul J. Wooldridge, Ronald C. Cohen, Kenneth S. Docherty, J. Alex Huffman, Suzane S. de Sá, Scot T. Martin, and Jose L. Jimenez
Atmos. Meas. Tech., 15, 459–483, https://doi.org/10.5194/amt-15-459-2022, https://doi.org/10.5194/amt-15-459-2022, 2022
Short summary
Short summary
Particle-phase nitrates are an important component of atmospheric aerosols and chemistry. In this paper, we systematically explore the application of aerosol mass spectrometry (AMS) to quantify the organic and inorganic nitrate fractions of aerosols in the atmosphere. While AMS has been used for a decade to quantify nitrates, methods are not standardized. We make recommendations for a more universal approach based on this analysis of a large range of field and laboratory observations.
Markus D. Petters
Atmos. Meas. Tech., 14, 7909–7928, https://doi.org/10.5194/amt-14-7909-2021, https://doi.org/10.5194/amt-14-7909-2021, 2021
Short summary
Short summary
Inverse methods infer physical properties from a measured instrument response. Measurement noise often interferes with the inversion. This work presents a general, domain-independent, accessible, and computationally efficient software implementation of a common class of statistical inversion methods. In addition, a new method to invert data from humidified tandem differential mobility analyzers is introduced. Results show that the approach is suitable for inversion of large-scale datasets.
Pak Lun Fung, Martha Arbayani Zaidan, Ola Surakhi, Sasu Tarkoma, Tuukka Petäjä, and Tareq Hussein
Atmos. Meas. Tech., 14, 5535–5554, https://doi.org/10.5194/amt-14-5535-2021, https://doi.org/10.5194/amt-14-5535-2021, 2021
Short summary
Short summary
Aerosol size distribution measurements rely on a variety of techniques to classify the aerosol size and measure the size distribution. However, due to the instrumental insufficiency and inversion limitations, the raw dataset contains missing gaps or negative values, which hinder further analysis. With a merged particle size distribution in Jordan, this paper suggests a neural network method to estimate number concentrations at a particular size bin by the number concentration at other size bins.
Xiansheng Liu, Hadiatullah Hadiatullah, Xun Zhang, L. Drew Hill, Andrew H. A. White, Jürgen Schnelle-Kreis, Jan Bendl, Gert Jakobi, Brigitte Schloter-Hai, and Ralf Zimmermann
Atmos. Meas. Tech., 14, 5139–5151, https://doi.org/10.5194/amt-14-5139-2021, https://doi.org/10.5194/amt-14-5139-2021, 2021
Short summary
Short summary
A monitoring campaign was conducted in Augsburg to determine a suitable noise reduction algorithm for the MA200 Aethalometer. Results showed that centred moving average (CMA) post-processing effectively removed spurious negative concentrations without major bias and reliably highlighted effects from local sources, effectively increasing spatio-temporal resolution in mobile measurements. Evaluation of each method on peak sample reduction and background correction further supports the reliability.
Jie Qiu, Wangshu Tan, Gang Zhao, Yingli Yu, and Chunsheng Zhao
Atmos. Meas. Tech., 14, 4879–4891, https://doi.org/10.5194/amt-14-4879-2021, https://doi.org/10.5194/amt-14-4879-2021, 2021
Short summary
Short summary
Considering nephelometers' major problems of a nonideal Lambertian light source and angle truncation, a new correction method based on a machine learning model is proposed. Our method has the advantage of obtaining data with high accuracy while achieving self-correction, which means that researchers can get more accurate scattering coefficients without the need for additional observation data. This method provides a more precise estimation of the aerosol’s direct radiative forcing.
Amir Yazdani, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 14, 4805–4827, https://doi.org/10.5194/amt-14-4805-2021, https://doi.org/10.5194/amt-14-4805-2021, 2021
Short summary
Short summary
We propose a spectroscopic method for estimating several mixture-averaged molecular properties (carbon number and molecular weight) in particulate matter relevant for understanding its chemical origins. This estimation is enabled by calibration models built and tested using laboratory standards containing molecules with known structure, and can be applied to filter samples of PM2.5 currently collected in existing air pollution monitoring networks and field campaigns.
Aki Virkkula
Atmos. Meas. Tech., 14, 3707–3719, https://doi.org/10.5194/amt-14-3707-2021, https://doi.org/10.5194/amt-14-3707-2021, 2021
Short summary
Short summary
The Aethalometer model is used widely for estimating the contributions of fossil fuel emissions and biomass burning to black carbon. The calculation is based on measured absorption Ångström exponents, which is ambiguous since it not only depends on the dominant absorber but also on the size and internal structure of the particles, core size, and shell thickness. The uncertainties of the fractions of absorption by eBC from fossil fuel and biomass burning are evaluated with a core–shell Mie model.
Weiqi Xu, Masayuki Takeuchi, Chun Chen, Yanmei Qiu, Conghui Xie, Wanyun Xu, Nan Ma, Douglas R. Worsnop, Nga Lee Ng, and Yele Sun
Atmos. Meas. Tech., 14, 3693–3705, https://doi.org/10.5194/amt-14-3693-2021, https://doi.org/10.5194/amt-14-3693-2021, 2021
Short summary
Short summary
Here we developed a method for estimation of particulate organic nitrates (pON) from the measurements of a high-resolution aerosol mass spectrometer coupled with a thermodenuder based on the volatility differences between inorganic nitrate and pON. The results generally had improvements in reducing negative values due to the influences of a high concentration of inorganic nitrate and a constant ratio of NO+ to NO2+ of organic nitrates (RON).
Melinda K. Schueneman, Benjamin A. Nault, Pedro Campuzano-Jost, Duseong S. Jo, Douglas A. Day, Jason C. Schroder, Brett B. Palm, Alma Hodzic, Jack E. Dibb, and Jose L. Jimenez
Atmos. Meas. Tech., 14, 2237–2260, https://doi.org/10.5194/amt-14-2237-2021, https://doi.org/10.5194/amt-14-2237-2021, 2021
Short summary
Short summary
This work focuses on two important properties of the aerosol, acidity, and sulfate composition, which is important for our understanding of aerosol health and environmental impacts. We explore different methods to understand the composition of the aerosol with measurements from a specific instrument and apply those methods to a large dataset. These measurements are confounded by other factors, making it challenging to predict aerosol sulfate composition; pH estimations, however, show promise.
Weilun Zhao, Wangshu Tan, Gang Zhao, Chuanyang Shen, Yingli Yu, and Chunsheng Zhao
Atmos. Meas. Tech., 14, 1319–1331, https://doi.org/10.5194/amt-14-1319-2021, https://doi.org/10.5194/amt-14-1319-2021, 2021
Chuanyang Shen, Gang Zhao, and Chunsheng Zhao
Atmos. Meas. Tech., 14, 1293–1301, https://doi.org/10.5194/amt-14-1293-2021, https://doi.org/10.5194/amt-14-1293-2021, 2021
Short summary
Short summary
Aerosol hygroscopicity measured by the humidified tandem differential mobility analyzer (HTDMA) is affected by multiply charged particles from two aspects: (1) number contribution and (2) the weakening effect. An algorithm is proposed to do the multi-charge correction and applied to a field measurement. Results show that the difference between corrected and measured size-resolved κ can reach 0.05, highlighting that special attention needs to be paid to the multi-charge effect when using HTDMA.
Francesco Canonaco, Anna Tobler, Gang Chen, Yulia Sosedova, Jay Gates Slowik, Carlo Bozzetti, Kaspar Rudolf Daellenbach, Imad El Haddad, Monica Crippa, Ru-Jin Huang, Markus Furger, Urs Baltensperger, and André Stephan Henry Prévôt
Atmos. Meas. Tech., 14, 923–943, https://doi.org/10.5194/amt-14-923-2021, https://doi.org/10.5194/amt-14-923-2021, 2021
Short summary
Short summary
Long-term ambient aerosol mass spectrometric data were analyzed with a statistical model (PMF) to obtain source contributions and fingerprints. The new aspects of this paper involve time-dependent source fingerprints by a rolling technique and the replacement of the full visual inspection of each run by a user-defined set of criteria to monitor the quality of each of these runs more efficiently. More reliable sources will finally provide better instruments for political mitigation strategies.
Kenji Miki and Shigeto Kawashima
Atmos. Meas. Tech., 14, 685–693, https://doi.org/10.5194/amt-14-685-2021, https://doi.org/10.5194/amt-14-685-2021, 2021
Short summary
Short summary
Laser optics have long been used in pollen counting systems. To clarify the limitations and potential new applications of laser optics for automatic pollen counting and discrimination, we determined the light scattering patterns of various pollen types, tracked temporal changes in these distributions, and introduced a new theory for automatic pollen discrimination.
Yinchao Zhang, Su Chen, Siying Chen, He Chen, and Pan Guo
Atmos. Meas. Tech., 13, 6675–6689, https://doi.org/10.5194/amt-13-6675-2020, https://doi.org/10.5194/amt-13-6675-2020, 2020
Short summary
Short summary
Air pollution has an important impact on human health, climatic patterns, and the ecological environment. The complexity of the nocturnal boundary layer (NBL), combined with its strong physio-chemical effect, induces worse polluted episodes. Therefore, we present a new approach named cluster analysis of gradient method (CA-GM) to overcome the multilayer structure and remove the fluctuation of NBL height using raw data resolution.
Tommy Chan, Runlong Cai, Lauri R. Ahonen, Yiliang Liu, Ying Zhou, Joonas Vanhanen, Lubna Dada, Yan Chao, Yongchun Liu, Lin Wang, Markku Kulmala, and Juha Kangasluoma
Atmos. Meas. Tech., 13, 4885–4898, https://doi.org/10.5194/amt-13-4885-2020, https://doi.org/10.5194/amt-13-4885-2020, 2020
Short summary
Short summary
Using a particle size magnifier (PSM; Airmodus, Finland), we determined the particle size distribution using four inversion methods and compared each method to the others to establish their strengths and weaknesses. Furthermore, we provided a step-by-step procedure on how to invert measured data using the PSM. Finally, we provided recommendations, code and data related to the data inversion. This is an important paper, as no operating procedure exists regarding how to process measured PSM data.
Paolo Laj, Alessandro Bigi, Clémence Rose, Elisabeth Andrews, Cathrine Lund Myhre, Martine Collaud Coen, Yong Lin, Alfred Wiedensohler, Michael Schulz, John A. Ogren, Markus Fiebig, Jonas Gliß, Augustin Mortier, Marco Pandolfi, Tuukka Petäja, Sang-Woo Kim, Wenche Aas, Jean-Philippe Putaud, Olga Mayol-Bracero, Melita Keywood, Lorenzo Labrador, Pasi Aalto, Erik Ahlberg, Lucas Alados Arboledas, Andrés Alastuey, Marcos Andrade, Begoña Artíñano, Stina Ausmeel, Todor Arsov, Eija Asmi, John Backman, Urs Baltensperger, Susanne Bastian, Olaf Bath, Johan Paul Beukes, Benjamin T. Brem, Nicolas Bukowiecki, Sébastien Conil, Cedric Couret, Derek Day, Wan Dayantolis, Anna Degorska, Konstantinos Eleftheriadis, Prodromos Fetfatzis, Olivier Favez, Harald Flentje, Maria I. Gini, Asta Gregorič, Martin Gysel-Beer, A. Gannet Hallar, Jenny Hand, Andras Hoffer, Christoph Hueglin, Rakesh K. Hooda, Antti Hyvärinen, Ivo Kalapov, Nikos Kalivitis, Anne Kasper-Giebl, Jeong Eun Kim, Giorgos Kouvarakis, Irena Kranjc, Radovan Krejci, Markku Kulmala, Casper Labuschagne, Hae-Jung Lee, Heikki Lihavainen, Neng-Huei Lin, Gunter Löschau, Krista Luoma, Angela Marinoni, Sebastiao Martins Dos Santos, Frank Meinhardt, Maik Merkel, Jean-Marc Metzger, Nikolaos Mihalopoulos, Nhat Anh Nguyen, Jakub Ondracek, Noemi Pérez, Maria Rita Perrone, Jean-Eudes Petit, David Picard, Jean-Marc Pichon, Veronique Pont, Natalia Prats, Anthony Prenni, Fabienne Reisen, Salvatore Romano, Karine Sellegri, Sangeeta Sharma, Gerhard Schauer, Patrick Sheridan, James Patrick Sherman, Maik Schütze, Andreas Schwerin, Ralf Sohmer, Mar Sorribas, Martin Steinbacher, Junying Sun, Gloria Titos, Barbara Toczko, Thomas Tuch, Pierre Tulet, Peter Tunved, Ville Vakkari, Fernando Velarde, Patricio Velasquez, Paolo Villani, Sterios Vratolis, Sheng-Hsiang Wang, Kay Weinhold, Rolf Weller, Margarita Yela, Jesus Yus-Diez, Vladimir Zdimal, Paul Zieger, and Nadezda Zikova
Atmos. Meas. Tech., 13, 4353–4392, https://doi.org/10.5194/amt-13-4353-2020, https://doi.org/10.5194/amt-13-4353-2020, 2020
Short summary
Short summary
The paper establishes the fiducial reference of the GAW aerosol network providing the fully characterized value chain to the provision of four climate-relevant aerosol properties from ground-based sites. Data from almost 90 stations worldwide are reported for a reference year, 2017, providing a unique and very robust view of the variability of these variables worldwide. Current gaps in the GAW network are analysed and requirements for the Global Climate Monitoring System are proposed.
Shengqiang Zhu, Lei Li, Shurong Wang, Mei Li, Yaxi Liu, Xiaohui Lu, Hong Chen, Lin Wang, Jianmin Chen, Zhen Zhou, Xin Yang, and Xiaofei Wang
Atmos. Meas. Tech., 13, 4111–4121, https://doi.org/10.5194/amt-13-4111-2020, https://doi.org/10.5194/amt-13-4111-2020, 2020
Short summary
Short summary
Single-particle aerosol mass spectrometry (SPAMS) is widely used to detect chemical compositions and sizes of individual aerosol particles. However, it has a major issue: the mass accuracy of high-resolution SPAMS is relatively low. Here we developed an automatic linear calibration method to greatly improve the mass accuracy of SPAMS spectra so that the elemental compositions of organic peaks, such as Cx, CxHy, CxHyOz and CxHyNO peaks, can be directly identified just based on their m / z values.
William Wandji Nyamsi, Antti Lipponen, Arturo Sanchez-Lorenzo, Martin Wild, and Antti Arola
Atmos. Meas. Tech., 13, 3061–3079, https://doi.org/10.5194/amt-13-3061-2020, https://doi.org/10.5194/amt-13-3061-2020, 2020
Short summary
Short summary
This paper proposes a novel and accurate method for estimating and reconstructing aerosol optical depth from sunshine duration measurements under cloud-free conditions at any place and time since the late 19th century. The method performs very well when compared to AErosol RObotic NETwork measurements and operates an efficient detection of signals from massive volcanic eruptions. Reconstructed long-term aerosol optical depths are in agreement with the dimming/brightening phenomenon.
Sascha Pfeifer, Thomas Müller, Andrew Freedman, and Alfred Wiedensohler
Atmos. Meas. Tech., 13, 2161–2167, https://doi.org/10.5194/amt-13-2161-2020, https://doi.org/10.5194/amt-13-2161-2020, 2020
Short summary
Short summary
The effect of the baseline drift on the resulting extinction values of three CAPS PMex monitors with different wavelengths was analysed for an urban background station. A significant baseline drift was observed, which leads to characteristic measurement artefacts for particle extinction. Two alternative methods for recalculating the baseline are shown. With these methods the extinction artefacts are diminished and the effective scattering of the resulting extinction values is reduced.
Stuart K. Grange, Hanspeter Lötscher, Andrea Fischer, Lukas Emmenegger, and Christoph Hueglin
Atmos. Meas. Tech., 13, 1867–1885, https://doi.org/10.5194/amt-13-1867-2020, https://doi.org/10.5194/amt-13-1867-2020, 2020
Short summary
Short summary
Black carbon (BC) is an important atmospheric pollutant and can be monitored by instruments called aethalometers. A pragmatic data processing technique called the
aethalometer modelcan be used to apportion aethalometer observations into traffic and woodburning components. We present an exploratory data analysis evaluating the aethalometer model and use the outputs for BC trend analysis across Switzerland. The aethalometer model's robustness and utility for such analyses is discussed.
Charlotte Bürki, Matteo Reggente, Ann M. Dillner, Jenny L. Hand, Stephanie L. Shaw, and Satoshi Takahama
Atmos. Meas. Tech., 13, 1517–1538, https://doi.org/10.5194/amt-13-1517-2020, https://doi.org/10.5194/amt-13-1517-2020, 2020
Short summary
Short summary
Infrared spectroscopy is a chemically informative method for particulate matter characterization. However, recent work has demonstrated that predictions depend heavily on the choice of calibration model parameters. We propose a means for managing parameter uncertainties by combining available data from laboratory standards, molecular databases, and collocated ambient measurements to provide useful characterization of atmospheric organic matter on a large scale.
Kaixu Bai, Ke Li, Jianping Guo, Yuanjian Yang, and Ni-Bin Chang
Atmos. Meas. Tech., 13, 1213–1226, https://doi.org/10.5194/amt-13-1213-2020, https://doi.org/10.5194/amt-13-1213-2020, 2020
Short summary
Short summary
A novel gap-filling method called the diurnal-cycle-constrained empirical orthogonal function (DCCEOF) is proposed. Cross validation indicates that this method gives high accuracy in predicting missing values in daily PM2.5 time series by accounting for the local diurnal phases, especially by reconstructing daily extrema that cannot be accurately restored by other approaches. The DCCEOF method can be easily applied to other data sets because of its self-consistent capability.
Tongshu Zheng, Michael H. Bergin, Ronak Sutaria, Sachchida N. Tripathi, Robert Caldow, and David E. Carlson
Atmos. Meas. Tech., 12, 5161–5181, https://doi.org/10.5194/amt-12-5161-2019, https://doi.org/10.5194/amt-12-5161-2019, 2019
Short summary
Short summary
Here we present a simultaneous Gaussian process regression (GPR) and linear regression pipeline to calibrate and monitor dense wireless low-cost particulate matter sensor networks (WLPMSNs) on the fly by using all available reference monitors across an area. Our approach can achieve an overall 30 % prediction error at a 24 h scale, can differentiate malfunctioning nodes, and track drift. Our solution can substantially reduce manual labor for managing WLPMSNs and prolong their lifetimes.
Cited articles
Anderson, B. J., Musicant, D. R., Ritz, A. M., Ault, A., Gross, D. S., Yuen, M., and Gälli, M.: User-friendly clustering for atmospheric data analysis, Carleton College, Northfield, MN, USA, 2005.
Ankerst, M., Breunig, M. M., Kriegel, H.-P., and Sander, J.: OPTICS: Ordering points to identify the clustering structure, Sigmod Rec., 28, 49–60, https://doi.org/10.1145/304181.304187, 1999.
Arndt, J., Healy, R. M., Setyan, A., Flament, P., Deboudt, K., Riffault, V., Alleman, L. Y., Mbengue, S., and Wenger, J. C.: Characterization and source apportionment of single particles from metalworking activities, Environ. Pollut., 270, 116078, https://doi.org/10.1016/j.envpol.2020.116078, 2021.
Ault, A. P., Moore, M. J., Furutani, H., and Prather, K. A.: Impact of Emissions from the Los Angeles Port Region on San Diego Air Quality during Regional Transport Events, Environ. Sci. Technol., 43, 3500–3506, https://doi.org/10.1021/es8018918, 2009.
Awad, M. and Khanna, R.: Efficient learning machines: theories, concepts, and applications for engineers and system designers, Berkeley, CA Apress Berkeley, CA, https://doi.org/10.1007/978-1-4302-5990-9, 2015.
Bishop, C. M.: Pattern recognition and machine learning, Springer, New York, ISBN 978-0-387-31073-2, 2006.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001.
Carpenter, G. A., Grossberg, S., and Rosen, D. B.: ART 2-A: An adaptive resonance algorithm for rapid category learning and recognition, Neural Networks, 4, 493–504, https://doi.org/10.1016/0893-6080(91)90045-7, 1991.
Chang-Graham, A. L., Profeta, L. T., Johnson, T. J., Yokelson, R. J., Laskin, A., and Laskin, J.: Case study of water-soluble metal containing organic constituents of biomass burning aerosol, Environ. Sci. Technol., 45, 1257–1263, https://doi.org/10.1021/es103010j, 2011.
Christopoulos, C. D., Garimella, S., Zawadowicz, M. A., Möhler, O., and Cziczo, D. J.: A machine learning approach to aerosol classification for single-particle mass spectrometry, Atmos. Meas. Tech., 11, 5687–5699, https://doi.org/10.5194/amt-11-5687-2018, 2018.
Dall'Osto, M. and Harrison, R.: Chemical characterisation of single airborne particles in Athens (Greece) by ATOFMS, Atmos. Environ., 40, 7614–7631, https://doi.org/10.1016/j.atmosenv.2006.06.053, 2006.
Dall'Osto, M. and Harrison, R. M.: Urban organic aerosols measured by single particle mass spectrometry in the megacity of London, Atmos. Chem. Phys., 12, 4127–4142, https://doi.org/10.5194/acp-12-4127-2012, 2012.
Dall'Osto, M., Ovadnevaite, J., Ceburnis, D., Martin, D., Healy, R. M., O'Connor, I. P., Kourtchev, I., Sodeau, J. R., Wenger, J. C., and O'Dowd, C.: Characterization of urban aerosol in Cork city (Ireland) using aerosol mass spectrometry, Atmos. Chem. Phys., 13, 4997–5015, https://doi.org/10.5194/acp-13-4997-2013, 2013.
Dall'Osto, M., Beddows, D. C. S., McGillicuddy, E. J., Esser-Gietl, J. K., Harrison, R. M., and Wenger, J. C.: On the simultaneous deployment of two single-particle mass spectrometers at an urban background and a roadside site during SAPUSS, Atmos. Chem. Phys., 16, 9693–9710, https://doi.org/10.5194/acp-16-9693-2016, 2016.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, 2–4 August 1996, 226–231, https://doi.org/10.5555/3001460, 1996.
Ettaouil, M. and Ghanou, Y.: Neural architectures optimization and Genetic algorithms, WSEAS Trans. Comput., 8, 526–537, 2009.
Furutani, H., Jung, J., Miura, K., Takami, A., Kato, S., Kajii, Y., and Uematsu, M.: Single-particle chemical characterization and source apportionment of iron-containing atmospheric aerosols in Asian outflow, J. Geophys. Res., 116, D18204, https://doi.org/10.1029/2011JD015867, 2011.
Ge, G. and Wong, G. W.: Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles, BMC Bioinform., 9, 275, https://doi.org/10.1186/1471-2105-9-275, 2008.
Gross, D. S., Gälli, M. E., Silva, P. J., and Prather, K. A.: Relative Sensitivity Factors for Alkali Metal and Ammonium Cations in Single-Particle Aerosol Time-of-Flight Mass Spectra, Anal. Chem., 72, 416–422, https://doi.org/10.1021/ac990434g, 2000.
Harrison, R. M. and Yin, J.: Particulate matter in the atmosphere: which particle properties are important for its effects on health?, Sci. Total Environ., 249, 85–101, https://doi.org/10.1016/S0048-9697(99)00513-6, 2000.
Healy, R. M., Hellebust, S., Kourtchev, I., Allanic, A., O'Connor, I. P., Bell, J. M., Healy, D. A., Sodeau, J. R., and Wenger, J. C.: Source apportionment of PM2.5 in Cork Harbour, Ireland using a combination of single particle mass spectrometry and quantitative semi-continuous measurements, Atmos. Chem. Phys., 10, 9593–9613, https://doi.org/10.5194/acp-10-9593-2010, 2010.
Healy, R. M., Sciare, J., Poulain, L., Kamili, K., Merkel, M., Müller, T., Wiedensohler, A., Eckhardt, S., Stohl, A., Sarda-Estève, R., McGillicuddy, E., O'Connor, I. P., Sodeau, J. R., and Wenger, J. C.: Sources and mixing state of size-resolved elemental carbon particles in a European megacity: Paris, Atmos. Chem. Phys., 12, 1681–1700, https://doi.org/10.5194/acp-12-1681-2012, 2012.
Köllner, F., Schneider, J., Willis, M. D., Klimach, T., Helleis, F., Bozem, H., Kunkel, D., Hoor, P., Burkart, J., Leaitch, W. R., Aliabadi, A. A., Abbatt, J. P. D., Herber, A. B., and Borrmann, S.: Particulate trimethylamine in the summertime Canadian high Arctic lower troposphere, Atmos. Chem. Phys., 17, 13747–13766, https://doi.org/10.5194/acp-17-13747-2017, 2017.
Li, H., Qi, F., and Wang, S.: A comparison of model selection methods for multi-class support vector machines, in: Computational Science and Its Applications–ICCSA 2005: International Conference, Singapore, 9-12 May 2005, Proceedings, Part IV 5, 1140–1148, 2005.
Li, J., Pósfai, M., Hobbs, P. V., and Buseck, P. R.: Individual aerosol particles from biomass burning in southern Africa: 2, Compositions and aging of inorganic particles, J. Geophys. Res., 108, D13, https://doi.org/10.1029/2002JD002310, 2003.
Li, L., Huang, Z., Dong, J., Li, M., Gao, W., Nian, H., Fu, Z., Zhang, G., Bi, X., Cheng, P., and Zhou, Z.: Real time bipolar time-of-flight mass spectrometer for analyzing single aerosol particles, Int. J. Mass Spectrom., 303, 118–124, https://doi.org/10.1016/j.ijms.2011.01.017, 2011.
Li, L., Li, M., Huang, Z., Gao, W., Nian, H., Fu, Z., Gao, J., Chai, F., and Zhou, Z.: Ambient particle characterization by single particle aerosol mass spectrometry in an urban area of Beijing, Atmos. Environ., 94, 323–331, https://doi.org/10.1016/j.atmosenv.2014.03.048, 2014.
Liu, X., Van Espen, P., Adams, F., Cafmeyer, J., and Maenhaut, W.: Biomass burning in southern Africa: Individual particle characterization of atmospheric aerosols and savanna fire samples, J. Atmos. Chem., 36, 135–155, https://doi.org/10.1023/A:1006387031927, 2000.
Liu, Z., Lu, X., Feng, J., Fan, Q., Zhang, Y., and Yang, X.: Influence of Ship Emissions on Urban Air Quality: A Comprehensive Study Using Highly Time-Resolved Online Measurements and Numerical Simulation in Shanghai, Environ. Sci. Technol., 51, 202–211, https://doi.org/10.1021/acs.est.6b03834, 2017.
MacQueen, J.: Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, 281–297, 1967
Maynard, R.: Key airborne pollutants—the impact on health, Sci. Total Environ., 334–335, 9–13, https://doi.org/10.1016/j.scitotenv.2004.04.025, 2004.
Mitchell, T. M.: Machine Learning, McGraw-Hill, New York, ISBN 978-0-070-42807-2, 1997.
Moffet, R. C., de Foy, B., Molina, L. T., Molina, M. J., and Prather, K. A.: Measurement of ambient aerosols in northern Mexico City by single particle mass spectrometry, Atmos. Chem. Phys., 8, 4499–4516, https://doi.org/10.5194/acp-8-4499-2008, 2008.
Murphy, D. M.: The design of single particle laser mass spectrometers, Mass Spectrom. Rev., 26, 150–165, https://doi.org/10.1002/mas.20113, 2007.
Murphy, D. M., Middlebrook, A. M., and Warshawsky, M.: Cluster Analysis of Data from the Particle Analysis by Laser Mass Spectrometry (PALMS) Instrument, Aerosol Sci. Tech., 37, 382–391, https://doi.org/10.1080/02786820300971, 2003.
Noble, W. S.: What is a support vector machine?, Nat. Biotechnol., 24, 1565–1567, https://doi.org/10.1038/nbt1206-1565, 2006.
Passig, J. and Zimmermann, R.: Laser Ionization in Single-Particle Mass Spectrometry, in: Photoionization and Photo-Induced Processes in Mass Spectrometry, edited by: Zimmermann, R. and Hanley, L., Wiley, 359–411, https://doi.org/10.1002/9783527682201.ch11, 2021.
Passig, J., Schade, J., Rosewig, E. I., Irsig, R., Kröger-Badge, T., Czech, H., Sklorz, M., Streibel, T., Li, L., Li, X., Zhou, Z., Fallgren, H., Moldanova, J., and Zimmermann, R.: Resonance-enhanced detection of metals in aerosols using single-particle mass spectrometry, Atmos. Chem. Phys., 20, 7139–7152, https://doi.org/10.5194/acp-20-7139-2020, 2020.
Passig, J., Schade, J., Irsig, R., Li, L., Li, X., Zhou, Z., Adam, T., and Zimmermann, R.: Detection of ship plumes from residual fuel operation in emission control areas using single-particle mass spectrometry, Atmos. Meas. Tech., 14, 4171–4185, https://doi.org/10.5194/amt-14-4171-2021, 2021.
Passig, J., Schade, J., Irsig, R., Kröger-Badge, T., Czech, H., Adam, T., Fallgren, H., Moldanova, J., Sklorz, M., Streibel, T., and Zimmermann, R.: Single-particle characterization of polycyclic aromatic hydrocarbons in background air in northern Europe, Atmos. Chem. Phys., 22, 1495–1514, https://doi.org/10.5194/acp-22-1495-2022, 2022.
Pratt, K. A. and Prather, K. A.: Mass spectrometry of atmospheric aerosols—Recent developments and applications. Part I: Off-line mass spectrometry techniques, Mass Spectrom. Rev., 31, 1–16, 2012.
Ramchoun, H., Amine, M., Idrissi, J., Ghanou, Y., and Ettaouil, M.: Multilayer Perceptron: Architecture Optimization and Training, IJIMAI, 4, 26, https://doi.org/10.9781/ijimai.2016.415, 2016.
Rebotier, T. P. and Prather, K. A.: Aerosol time-of-flight mass spectrometry data analysis: A benchmark of clustering algorithms, Anal. Chim. Acta, 585, 38–54, https://doi.org/10.1016/j.aca.2006.12.009, 2007.
Reitz, P., Zorn, S. R., Trimborn, S. H., and Trimborn, A. M.: A new, powerful technique to analyze single particle aerosol mass spectra using a combination of OPTICS and the fuzzy c-means algorithm, J. Aerosol Sci., 98, 1–14, https://doi.org/10.1016/j.jaerosci.2016.04.003, 2016.
Schade, J., Passig, J., Irsig, R., Ehlert, S., Sklorz, M., Adam, T., Li, C., Rudich, Y., and Zimmermann, R.: Spatially Shaped Laser Pulses for the Simultaneous Detection of Polycyclic Aromatic Hydrocarbons as well as Positive and Negative Inorganic Ions in Single Particle Mass Spectrometry, Anal. Chem., 91, 10282–10288, https://doi.org/10.1021/acs.analchem.9b02477, 2019.
Segaran, T.: Programming collective intelligence: building smart web 2.0 applications, O’Reilly Media, Inc., Sebastopol, CA, USA, ISBN 978-0-596-52932-1, 2007.
Shen, X., Saathoff, H., Huang, W., Mohr, C., Ramisetty, R., and Leisner, T.: Understanding atmospheric aerosol particles with improved particle identification and quantification by single-particle mass spectrometry, Atmos. Meas. Tech., 12, 2219–2240, https://doi.org/10.5194/amt-12-2219-2019, 2019.
Shields, L. G., Suess, D. T., and Prather, K. A.: Determination of single particle mass spectral signatures from heavy-duty diesel vehicle emissions for PM2.5 source apportionment, Atmos. Environ., 41, 3841–3852, https://doi.org/10.1016/j.atmosenv.2007.01.025 , 2007.
Song, X.-H., Hopke, P. K., Fergenson, D. P., and Prather, K. A.: Classification of Single Particles Analyzed by ATOFMS Using an Artificial Neural Network, ART-2A, Anal. Chem., 71, 860–865, https://doi.org/10.1021/ac9809682, 1999.
Spencer, M. T., Shields, L. G., Sodeman, D. A., Toner, S. M., and Prather, K. A.: Comparison of oil and fuel particle chemical signatures with particle emissions from heavy and light duty vehicles, Atmos. Environ., 40, 5224–5235, https://doi.org/10.1016/j.atmosenv.2006.04.011, 2006.
Strehl, A., Ghosh, J., and Mooney, R.: Impact of Similarity Measures on Web-page Clustering, Workshop on Artificial Intelligence for Web Search (AAAI 2000), 30–31 July 2000, in: AAAI Technical Report WS-00-01, 2000, 58–64, https://doi.org/10.1609/aimag.v22i1.1552, 2000.
Tibshirani, R., Walther, G., and Hastie, T.: Estimating the number of clusters in a data set via the gap statistic, J. R. Stat. Soc. B, 63, 411–423, https://doi.org/10.1111/1467-9868.00293, 2001.
Toner, S. M., Sodeman, D. A., and Prather, K. A.: Single Particle Characterization of Ultrafine and Accumulation Mode Particles from Heavy Duty Diesel Vehicles Using Aerosol Time-of-Flight Mass Spectrometry, Environ. Sci. Technol., 40, 3912–3921, https://doi.org/10.1021/es051455x, 2006.
Wang, G.: GuanzhongLRT2/Machine-learning-approaches-for-automatic-classification-of-single-particle-mass-spectrometry-data: Machine learning approaches for automatic classification of SPMS data, Zenodo [code], https://doi.org/10.5281/zenodo.10402040, 2023.
Xiao, Q., Li, M., Liu, H., Fu, M., Deng, F., Lv, Z., Man, H., Jin, X., Liu, S., and He, K.: Characteristics of marine shipping emissions at berth: profiles for particulate matter and volatile organic compounds, Atmos. Chem. Phys., 18, 9527–9545, https://doi.org/10.5194/acp-18-9527-2018, 2018.
Zelenyuk, A., Imre, D., Cai, Y., Mueller, K., Han, Y., and Imrich, P.: SpectraMiner, an interactive data mining and visualization software for single particle mass spectroscopy: A laboratory test case, Int. J. Mass Spectrom., 258, 58–73, https://doi.org/10.1016/j.ijms.2006.06.015, 2006.
Zhao, W., Hopke, P. K., and Prather, K. A.: Comparison of two cluster analysis methods using single particle mass spectra, Atmos. Environ., 42, 881–892, https://doi.org/10.1016/j.atmosenv.2007.10.024, 2008.
Zhong, S.: Efficient Online Sphercal K-means Clustering, Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, jii1 – 4 August 2005, 3180–3185, 2005.
Zhou, L., Hopke, P. K., and Venkatachari, P.: Cluster analysis of single particle mass spectra measured at Flushing, NY, Anal. Chim. Acta, 555, 47–56, https://doi.org/10.1016/j.aca.2005.08.061, 2006.
Short summary
This research aims to develop a novel warning system for the real-time monitoring of pollutants in the atmosphere. The system is capable of sampling and investigating airborne aerosol particles on-site, utilizing artificial intelligence to learn their chemical signatures and to classify them in real time. We applied single-particle mass spectrometry for analyzing the chemical composition of aerosol particles and suggest several supervised algorithms for highly reliable automatic classification.
This research aims to develop a novel warning system for the real-time monitoring of pollutants...
