Articles | Volume 5, issue 1
Atmos. Meas. Tech., 5, 195–224, 2012
https://doi.org/10.5194/amt-5-195-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 5, 195–224, 2012
https://doi.org/10.5194/amt-5-195-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article 25 Jan 2012
Research article | 25 Jan 2012
Three-dimensional factorization of size-resolved organic aerosol mass spectra from Mexico City
I. M. Ulbrich et al.
Related subject area
Subject: Aerosols | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
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
Determination of black carbon mass concentration from aerosol light absorption using variable mass absorption cross-section
Assessment of particle size magnifier inversion methods to obtain the particle size distribution from atmospheric measurements
Effects of Multi-Charge on Aerosol Hygroscopicity Measurement by HTDMA
Aerosol pH Indicator and Organosulfate Detectability from Aerosol Mass Spectrometry 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 new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to one year of organic aerosol data
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
Estimating mean molecular weight, carbon number, and OM/OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network
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
Methods for identifying aged ship plumes and estimating contribution to aerosol exposure downwind of shipping lanes
Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps
An open platform for Aerosol InfraRed Spectroscopy analysis – AIRSpec
Understanding atmospheric aerosol particles with improved particle identification and quantification by single-particle mass spectrometry
Multi-scale measurements of mesospheric aerosols and electrons during the MAXIDUSTY campaign
A new method of inferring the size, number density, and charge of mesospheric dust from its in situ collection by the DUSTY probe
Atmospheric particulate matter characterization by Fourier transform infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks
Data inversion methods to determine sub-3 nm aerosol size distributions using the particle size magnifier
A novel method for calculating ambient aerosol liquid water content based on measurements of a humidified nephelometer system
Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting
Improved source apportionment of organic aerosols in complex urban air pollution using the multilinear engine (ME-2)
On Aethalometer measurement uncertainties and an instrument correction factor for the Arctic
Aethalometer multiple scattering correction Cref for mineral dust aerosols
Comparison of different Aethalometer correction schemes and a reference multi-wavelength absorption technique for ambient aerosol data
FATES: a flexible analysis toolkit for the exploration of single-particle mass spectrometer data
Retrievals of aerosol optical and microphysical properties from Imaging Polar Nephelometer scattering measurements
A technique for rapid source apportionment applied to ambient organic aerosol measurements from a thermal desorption aerosol gas chromatograph (TAG)
A new approach for retrieving the UV–vis optical properties of ambient aerosols
Sampling strategies and post-processing methods for increasing the time resolution of organic aerosol measurements requiring long sample-collection times
Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: sparse methods for statistical selection of relevant absorption bands
An automated baseline correction protocol for infrared spectra of atmospheric aerosols collected on polytetrafluoroethylene (Teflon) filters
Notably improved inversion of differential mobility particle sizer data obtained under conditions of fluctuating particle number concentrations
Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol
On the interpretation of the loading correction of the aethalometer
Finding candidate locations for aerosol pollution monitoring at street level using a data-driven methodology
Atmospheric extinction in solar tower plants: absorption and broadband correction for MOR measurements
Plume-based analysis of vehicle fleet air pollutant emissions and the contribution from high emitters
Statistical precision of the intensities retrieved from constrained fitting of overlapping peaks in high-resolution mass spectra
Differences in aerosol absorption Ångström exponents between correction algorithms for a particle soot absorption photometer measured on the South African Highveld
Simultaneous retrieval of effective refractive index and density from size distribution and light-scattering data: weakly absorbing aerosol
Measuring the atmospheric organic aerosol volatility distribution: a theoretical analysis
Determination and analysis of in situ spectral aerosol optical properties by a multi-instrumental approach
Methods for estimating uncertainty in factor analytic solutions
Application of mobile aerosol and trace gas measurements for the investigation of megacity air pollution emissions: the Paris metropolitan area
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
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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
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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.
Weilun Zhao, Wangshu Tan, Gang Zhao, Chuanyang Shen, Yingli Yu, and Chunsheng Zhao
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-337, https://doi.org/10.5194/amt-2020-337, 2020
Revised manuscript accepted for AMT
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
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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.
Chuanyang Shen, Gang Zhao, and Chunsheng Zhao
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-338, https://doi.org/10.5194/amt-2020-338, 2020
Revised manuscript accepted for AMT
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Aerosol hygroscopicity measured by HTDMA is affected by multiply charged particles from two aspects: (1) number contribution and (2) the weakening effect. We propose an algorithm to do the multi-charge correction for aerosol's size-resolved κ based on these two effects and applied it in a field measurement. Results show that the difference between corrected and measured κ can reach as large as 0.05, highlighting that special attention needs to be paid to the multi-charge effect by HTDMA.
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. Discuss., https://doi.org/10.5194/amt-2020-339, https://doi.org/10.5194/amt-2020-339, 2020
Revised manuscript accepted for AMT
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This work focuses on two important properties of the aerosol, acidity and sulfate composition, which are 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.
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
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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
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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.
Francesco Canonaco, Anna Tobler, Gang Chen, Yulia Sosedova, Jay Gates Slowik, Carlo Bozzetti, Kaspar Rudolf Daellenbach, Imad ElHaddad, Monica Crippa, Ru-Jin Huang, Markus Furger, Urs Baltensperger, and André Stefan Henri Prévôt
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-204, https://doi.org/10.5194/amt-2020-204, 2020
Revised manuscript accepted for AMT
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Long-term ambient aerosol mass spectrometric data was analyzed with a statistical model (PMF) to obtain source contributions & fingerprints. The new aspects of this manuscript 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.
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
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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
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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
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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
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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.
Amir Yazdani, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-79, https://doi.org/10.5194/amt-2020-79, 2020
Revised manuscript accepted for AMT
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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.
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
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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
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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.
Stina Ausmeel, Axel Eriksson, Erik Ahlberg, and Adam Kristensson
Atmos. Meas. Tech., 12, 4479–4493, https://doi.org/10.5194/amt-12-4479-2019, https://doi.org/10.5194/amt-12-4479-2019, 2019
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We present a method for identifying individual exhaust plumes of air pollution emitted from shipping, by linking these to specific ships using identification information which all ships transmit. We also quantify the contribution of these plumes to local particle levels, which has relevance for health effects. Ships emit a lot of nanometre-sized particles, which proved to be a good indicator of plumes at a distance of about 10 km downwind of a shipping lane in the Baltic Sea.
Ingrida Šaulienė, Laura Šukienė, Gintautas Daunys, Gediminas Valiulis, Lukas Vaitkevičius, Predrag Matavulj, Sanja Brdar, Marko Panic, Branko Sikoparija, Bernard Clot, Benoît Crouzy, and Mikhail Sofiev
Atmos. Meas. Tech., 12, 3435–3452, https://doi.org/10.5194/amt-12-3435-2019, https://doi.org/10.5194/amt-12-3435-2019, 2019
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The goal is to evaluate the capabilities of the new Rapid-E monitor and to construct a first-level pollen recognition algorithm. The output data were treated with ANN aiming at classification of the injected pollen. Algorithms based on scattering and fluorescence data alone fall short of acceptable quality. The combinations of these exceeded 80 % accuracy for 5 out of 11 pollen species. Constructing multistep algorithms with sequential discrimination of pollen can be a possible way forward.
Matteo Reggente, Rudolf Höhn, and Satoshi Takahama
Atmos. Meas. Tech., 12, 2313–2329, https://doi.org/10.5194/amt-12-2313-2019, https://doi.org/10.5194/amt-12-2313-2019, 2019
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The infrared spectra of atmospheric particles are rich in chemical information but require sophisticated statistical methods to extract information on account of their complex absorption profiles. We present an open software suite which makes current algorithms used for analysis of such spectra available to the community, with a browser-based interface for general users and modular architecture that facilitates addition of new methods by developers.
Xiaoli Shen, Harald Saathoff, Wei Huang, Claudia Mohr, Ramakrishna Ramisetty, and Thomas Leisner
Atmos. Meas. Tech., 12, 2219–2240, https://doi.org/10.5194/amt-12-2219-2019, https://doi.org/10.5194/amt-12-2219-2019, 2019
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Based on single-particle mass spectra from field measurements in the upper Rhine valley, we identified characteristic particle classes and estimated their mass contributions without the need of a reference instrument in the field. Our study provides a good example for quantitative interpretation of single-particle data. Together with the complimentary results from bulk measurements, we have shown how a better understanding of the mixing state of ambient aerosol particles can be achieved.
Tarjei Antonsen, Ove Havnes, and Andres Spicher
Atmos. Meas. Tech., 12, 2139–2153, https://doi.org/10.5194/amt-12-2139-2019, https://doi.org/10.5194/amt-12-2139-2019, 2019
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This paper presents measurements of changes in mesospheric aerosol populations on different length scales, as detected by the DUSTY and MUDD probes on the MAXIDUSTY-1B rocket on 8 July 2016. Identical probes recorded very different currents, which we attribute to adverse flow effects. We find a general anti-correlation for charged aerosols and electrons, but not consistently on all length scales. We conclude that there is no simple relationship between aerosols and PMSE (radar echoes).
Ove Havnes, Tarjei Antonsen, Gerd Baumgarten, Thomas W. Hartquist, Alexander Biebricher, Åshild Fredriksen, Martin Friedrich, and Jonas Hedin
Atmos. Meas. Tech., 12, 1673–1683, https://doi.org/10.5194/amt-12-1673-2019, https://doi.org/10.5194/amt-12-1673-2019, 2019
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We present a new method of analyzing data from rocket-borne aerosol detectors of the Faraday cup type (DUSTY). By using models for how aerosols are charged in the mesosphere and how they interact in a collision with the probes, fundamental parameters like aerosol radius, charge, and number density can be derived. The resolution can be down to ~ 10 cm, which is much lower than other available methods. The theory is furthermore used to analyze DUSTY data from the 2016 rocket campaign MAXIDUSTY.
Satoshi Takahama, Ann M. Dillner, Andrew T. Weakley, Matteo Reggente, Charlotte Bürki, Mária Lbadaoui-Darvas, Bruno Debus, Adele Kuzmiakova, and Anthony S. Wexler
Atmos. Meas. Tech., 12, 525–567, https://doi.org/10.5194/amt-12-525-2019, https://doi.org/10.5194/amt-12-525-2019, 2019
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Mid-infrared spectra of particulate matter (PM) samples are complex but chemically informative and present an opportunity for cost-effective measurement of PM provided that quantitative calibration models can be built. We review an emerging strategy for building statistical calibration models using collocated measurements, interpreting the physical bases for such models and evaluating the suitability of existing calibration models to new samples.
Runlong Cai, Dongsen Yang, Lauri R. Ahonen, Linlin Shi, Frans Korhonen, Yan Ma, Jiming Hao, Tuukka Petäjä, Jun Zheng, Juha Kangasluoma, and Jingkun Jiang
Atmos. Meas. Tech., 11, 4477–4491, https://doi.org/10.5194/amt-11-4477-2018, https://doi.org/10.5194/amt-11-4477-2018, 2018
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We tested the performance of four inversion methods to recover sub-3 nm aerosol size distributions using the particle size magnifier (PSM). The PSM is widely used in new particle formation study; however, the inversion methods used in previous studies may report false particle concentrations. Due to the results, we suggest using the expectation–maximization algorithm to address the PSM inversion problem. We also gave practical suggestions on PSM operation based on the inversion analysis.
Ye Kuang, Chun Sheng Zhao, Gang Zhao, Jiang Chuan Tao, Wanyun Xu, Nan Ma, and Yu Xuan Bian
Atmos. Meas. Tech., 11, 2967–2982, https://doi.org/10.5194/amt-11-2967-2018, https://doi.org/10.5194/amt-11-2967-2018, 2018
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Aerosol water has become an important topic recently because of its implications for multiphase secondary aerosol formation during severe haze events in Asia. This is a timely paper on this topic; a novel method is proposed to calculate ambient aerosol liquid water contents based only on measurements of a three-wavelength humidified nephelometer system. The advantage of this method is that this technique can provide continuous measurements of the changing ambient conditions.
Cheng Wu and Jian Zhen Yu
Atmos. Meas. Tech., 11, 1233–1250, https://doi.org/10.5194/amt-11-1233-2018, https://doi.org/10.5194/amt-11-1233-2018, 2018
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A new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator is proposed to conduct benchmark tests on a variety of linear regression techniques. With an appropriate weighting, Deming regression (DR), weighted ODR (WODR), and York regression (YR) are recommended for atmospheric studies when both x and y data have measurement errors. An Igor-based program (Scatter Plot) is developed to facilitate the regression implementation.
Qiao Zhu, Xiao-Feng Huang, Li-Ming Cao, Lin-Tong Wei, Bin Zhang, Ling-Yan He, Miriam Elser, Francesco Canonaco, Jay G. Slowik, Carlo Bozzetti, Imad El-Haddad, and André S. H. Prévôt
Atmos. Meas. Tech., 11, 1049–1060, https://doi.org/10.5194/amt-11-1049-2018, https://doi.org/10.5194/amt-11-1049-2018, 2018
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Organic aerosol constitutes one of the major components of atmospheric particulate matter globally and is emitted from various sources. Therefore, identifying and quantifying the sources of organic aerosol accurately is a key task in the field. In this study, we applied a rather novel procedure for an improved source apportionment method (ME-2) to resolve the
less meaningful or mixed factorsproblems for organic aerosol using the traditional method (PMF).
John Backman, Lauren Schmeisser, Aki Virkkula, John A. Ogren, Eija Asmi, Sandra Starkweather, Sangeeta Sharma, Konstantinos Eleftheriadis, Taneil Uttal, Anne Jefferson, Michael Bergin, Alexander Makshtas, Peter Tunved, and Markus Fiebig
Atmos. Meas. Tech., 10, 5039–5062, https://doi.org/10.5194/amt-10-5039-2017, https://doi.org/10.5194/amt-10-5039-2017, 2017
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Light absorption by aerosol particles is of climatic importance. A widely used means to measure aerosol light absorption is a filter-based measurement technique. In remote areas, such as the Arctic, filter-based instruments operate close to their detection limit. The study presents how a lower detection limit can be achieved for one such instrument, the Aethalometer. Additionally, the Aethalometer is compared to similar instruments, thus improving measurement inter-comparability in the Arctic.
Claudia Di Biagio, Paola Formenti, Mathieu Cazaunau, Edouard Pangui, Nicolas Marchand, and Jean-François Doussin
Atmos. Meas. Tech., 10, 2923–2939, https://doi.org/10.5194/amt-10-2923-2017, https://doi.org/10.5194/amt-10-2923-2017, 2017
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Mineral dust is one of the most abundant aerosol species at the global scale and an accurate estimation of its absorption at solar wavelengths is crucial to assess its impact on climate. In this work we provide an estimate of the Aethalometer multiple scattering correction for mineral dust aerosols at 450 and 660 nm. Our results suggest that the use of an optimized correction factor can lead to up to 11 % higher absorption coefficient and to 3 % higher single scattering albedo for mineral dust.
Jorge Saturno, Christopher Pöhlker, Dario Massabò, Joel Brito, Samara Carbone, Yafang Cheng, Xuguang Chi, Florian Ditas, Isabella Hrabě de Angelis, Daniel Morán-Zuloaga, Mira L. Pöhlker, Luciana V. Rizzo, David Walter, Qiaoqiao Wang, Paulo Artaxo, Paolo Prati, and Meinrat O. Andreae
Atmos. Meas. Tech., 10, 2837–2850, https://doi.org/10.5194/amt-10-2837-2017, https://doi.org/10.5194/amt-10-2837-2017, 2017
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Different Aethalometer correction schemes were compared to a multi-wavelength absorption reference measurement. One of the correction schemes was found to artificially increase the short-wavelength absorption coefficients. It was found that accounting for aerosol scattering properties in the correction is crucial to retrieve the proper absorption Ångström exponent (AAE). We found that the raw AAE of uncompensated Aethalometer attenuation significantly correlates with a measured reference AAE.
Camille M. Sultana, Gavin C. Cornwell, Paul Rodriguez, and Kimberly A. Prather
Atmos. Meas. Tech., 10, 1323–1334, https://doi.org/10.5194/amt-10-1323-2017, https://doi.org/10.5194/amt-10-1323-2017, 2017
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Single-particle mass spectrometers (SPMSs) can determine the size and chemical composition of single particles in real time. We developed the first open-source SPMS toolkit to allow creative script-based data mining along with GUI-based visual data exploration and calibration all within a single programming environment. We believe that this software will be adopted by many in the SPMS community and improve the efficiency of knowledge discovery from these atmospherically critical data sets.
W. Reed Espinosa, Lorraine A. Remer, Oleg Dubovik, Luke Ziemba, Andreas Beyersdorf, Daniel Orozco, Gregory Schuster, Tatyana Lapyonok, David Fuertes, and J. Vanderlei Martins
Atmos. Meas. Tech., 10, 811–824, https://doi.org/10.5194/amt-10-811-2017, https://doi.org/10.5194/amt-10-811-2017, 2017
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Aerosols, and their interaction with clouds, play a key role in the climate of our planet but many of their properties are poorly understood. We present a new method for estimating the size, shape and optical constants of atmospheric particles from light-scattering measurements made both in the laboratory and aboard an aircraft. This method is shown to have sufficient accuracy to potentially reduce existing uncertainties, particularly in airborne measurements.
Yaping Zhang, Brent J. Williams, Allen H. Goldstein, Kenneth S. Docherty, and Jose L. Jimenez
Atmos. Meas. Tech., 9, 5637–5653, https://doi.org/10.5194/amt-9-5637-2016, https://doi.org/10.5194/amt-9-5637-2016, 2016
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The binning method provides an alternate way to process GC–MS data in a very fast manner. It only takes a very small portion of time (days versus years) compared to the traditional GC–MS data analysis method (peak identification and integration). Furthermore, the binning method can also be applied to any data set from a measurement (mass spectrometry, spectroscopy, etc.) with additional separations (volatility, polarity, size, etc.).
Nir Bluvshtein, J. Michel Flores, Lior Segev, and Yinon Rudich
Atmos. Meas. Tech., 9, 3477–3490, https://doi.org/10.5194/amt-9-3477-2016, https://doi.org/10.5194/amt-9-3477-2016, 2016
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Understanding spectrally dependent optical properties of aerosols is needed to quantify the effective radiative forcing due to aerosol–radiation interactions. We describe a new approach to retrieve extensive and intensive optical properties of the aerosol population over 300 to 650 nm wavelength. This new approach was validated with retrieval simulations, laboratory and continuous ambient aerosols measurements. Results showed low errors and good agreement with expected values.
Rob L. Modini and Satoshi Takahama
Atmos. Meas. Tech., 9, 3337–3354, https://doi.org/10.5194/amt-9-3337-2016, https://doi.org/10.5194/amt-9-3337-2016, 2016
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Aerosol measurement techniques with high detection limits often result in poorly time-resolved measurements. We investigated sampling strategies and post-processing methods for constructing hourly resolved aerosol concentration time series from samples collected for 4 to 8 h. We show that this is an effective way to increase measurement time resolution, and that under realistic experimental conditions, simple methods can perform as well as more sophisticated methods.
Satoshi Takahama, Giulia Ruggeri, and Ann M. Dillner
Atmos. Meas. Tech., 9, 3429–3454, https://doi.org/10.5194/amt-9-3429-2016, https://doi.org/10.5194/amt-9-3429-2016, 2016
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We introduce the application of statistical algorithms that allow us to associate various dimensions of aerosol composition to vibrational modes measured by infrared absorption spectroscopy. We demonstrate their use on four organic functional groups for which absorption bands are known and extend the application to interpret bands associated with ambient organic carbon and elemental carbon quantified by an independent measurement technique that is widely used in aerosol monitoring networks.
Adele Kuzmiakova, Ann M. Dillner, and Satoshi Takahama
Atmos. Meas. Tech., 9, 2615–2631, https://doi.org/10.5194/amt-9-2615-2016, https://doi.org/10.5194/amt-9-2615-2016, 2016
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We describe a new method for removing Teflon substrate interference from ambient aerosol infrared spectra such that functional group quantification and spectral clustering (for source classification) can be applied. We demonstrate that this technique produces similar results to a more labor-intensive method used in many field campaigns over the past several years, but is simpler and better constrained by physical criteria that we impose, leading to the possibility of widespread adoption.
Bjarke Mølgaard, Jarno Vanhatalo, Pasi P. Aalto, Nønne L. Prisle, and Kaarle Hämeri
Atmos. Meas. Tech., 9, 741–751, https://doi.org/10.5194/amt-9-741-2016, https://doi.org/10.5194/amt-9-741-2016, 2016
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We have improved the reliability of submicron aerosol particle size distributions measured in urban locations. This improvement was obtained by processing the data in a new way and avoiding a problematic assumption of a stationary aerosol during each size distribution measurement.
I. Crawford, S. Ruske, D. O. Topping, and M. W. Gallagher
Atmos. Meas. Tech., 8, 4979–4991, https://doi.org/10.5194/amt-8-4979-2015, https://doi.org/10.5194/amt-8-4979-2015, 2015
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HCA analysis methods were evaluated for the purpose of identifying primary biological aerosol sampled with a WIBS. The ward linkage with z-score normalisation could discriminate between five test particles with 98% accuracy. We applied these methods to a previously studied ambient data set, where both methods produced similar results with some minor differences in cluster partitioning. Finally we compared to previous approaches and found our new method offered improved quantification of PBA.
A. Virkkula, X. Chi, A. Ding, Y. Shen, W. Nie, X. Qi, L. Zheng, X. Huang, Y. Xie, J. Wang, T. Petäjä, and M. Kulmala
Atmos. Meas. Tech., 8, 4415–4427, https://doi.org/10.5194/amt-8-4415-2015, https://doi.org/10.5194/amt-8-4415-2015, 2015
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Aerosol optical properties were measured with a seven-wavelength aethalometer and a three-wavelength nephelometer in Nanjing, China, in September 2013–January 2015. The aethalometer compensation parameter k depended on the backscatter fraction, measured with an independent method, the integrating nephelometer. The compensation parameter decreased with increasing single-scattering albedo.
V. Moosavi, G. Aschwanden, and E. Velasco
Atmos. Meas. Tech., 8, 3563–3575, https://doi.org/10.5194/amt-8-3563-2015, https://doi.org/10.5194/amt-8-3563-2015, 2015
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Complexity of urban environments makes the problem of locating air quality monitoring stations at ground level challenging.
In this work a data-driven methodology is proposed where using Self Organizing Maps along with several urban parameters and few direct measurements of aerosols at the street level, the concentration of those aerosols in a larger area is estimated. Finally, via clustering of areas with similar urban patterns, the potential locations of monitoring stations are identified.
N. Hanrieder, S. Wilbert, R. Pitz-Paal, C. Emde, J. Gasteiger, B. Mayer, and J. Polo
Atmos. Meas. Tech., 8, 3467–3480, https://doi.org/10.5194/amt-8-3467-2015, https://doi.org/10.5194/amt-8-3467-2015, 2015
J. M. Wang, C.-H. Jeong, N. Zimmerman, R. M. Healy, D. K. Wang, F. Ke, and G. J. Evans
Atmos. Meas. Tech., 8, 3263–3275, https://doi.org/10.5194/amt-8-3263-2015, https://doi.org/10.5194/amt-8-3263-2015, 2015
M. J. Cubison and J. L. Jimenez
Atmos. Meas. Tech., 8, 2333–2345, https://doi.org/10.5194/amt-8-2333-2015, https://doi.org/10.5194/amt-8-2333-2015, 2015
J. Backman, A. Virkkula, V. Vakkari, J. P. Beukes, P. G. Van Zyl, M. Josipovic, S. Piketh, P. Tiitta, K. Chiloane, T. Petäjä, M. Kulmala, and L. Laakso
Atmos. Meas. Tech., 7, 4285–4298, https://doi.org/10.5194/amt-7-4285-2014, https://doi.org/10.5194/amt-7-4285-2014, 2014
E. Kassianov, J. Barnard, M. Pekour, L. K. Berg, J. Shilling, C. Flynn, F. Mei, and A. Jefferson
Atmos. Meas. Tech., 7, 3247–3261, https://doi.org/10.5194/amt-7-3247-2014, https://doi.org/10.5194/amt-7-3247-2014, 2014
E. Karnezi, I. Riipinen, and S. N. Pandis
Atmos. Meas. Tech., 7, 2953–2965, https://doi.org/10.5194/amt-7-2953-2014, https://doi.org/10.5194/amt-7-2953-2014, 2014
S. Segura, V. Estellés, G. Titos, H. Lyamani, M. P. Utrillas, P. Zotter, A. S. H. Prévôt, G. Močnik, L. Alados-Arboledas, and J. A. Martínez-Lozano
Atmos. Meas. Tech., 7, 2373–2387, https://doi.org/10.5194/amt-7-2373-2014, https://doi.org/10.5194/amt-7-2373-2014, 2014
P. Paatero, S. Eberly, S. G. Brown, and G. A. Norris
Atmos. Meas. Tech., 7, 781–797, https://doi.org/10.5194/amt-7-781-2014, https://doi.org/10.5194/amt-7-781-2014, 2014
S.-L. von der Weiden-Reinmüller, F. Drewnick, M. Crippa, A. S. H. Prévôt, F. Meleux, U. Baltensperger, M. Beekmann, and S. Borrmann
Atmos. Meas. Tech., 7, 279–299, https://doi.org/10.5194/amt-7-279-2014, https://doi.org/10.5194/amt-7-279-2014, 2014
Cited articles
Aiken, A. C., Decarlo, P. F., Kroll, J. H., Worsnop, D. R., Huffman, J. A., Docherty, K. S., Ulbrich, I. M., Mohr, C., Kimmel, J. R., Sueper, D., Sun, Y., Zhang, Q., Trimborn, A., Northway, M., Ziemann, P. J., Canagaratna, M. R., Onasch, T. B., Alfarra, M. R., Prevot, A. S. H., Dommen, J., Duplissy, J., Metzger, A., Baltensperger, U., and Jimenez, J. L.: O/C and OM/OC Ratios of Primary, Secondary, and Ambient Organic Aerosols with High-Resolution Time-of-Flight Aerosol Mass Spectrometry, Environ. Sci. Technol., 42, 4478–4485, https://doi.org/10.1021/es703009q, 2008.
Aiken, A. C., Salcedo, D., Cubison, M. J., Huffman, J. A., DeCarlo, P. F., Ulbrich, I. M., Docherty, K. S., Sueper, D., Kimmel, J. R., Worsnop, D. R., Trimborn, A., Northway, M., Stone, E. A., Schauer, J. J., Volkamer, R. M., Fortner, E., de Foy, B., Wang, J., Laskin, A., Shutthanandan, V., Zheng, J., Zhang, R., Gaffney, J., Marley, N. A., Paredes-Miranda, G., Arnott, W. P., Molina, L. T., Sosa, G., and Jimenez, J. L.: Mexico City aerosol analysis during MILAGRO using high resolution aerosol mass spectrometry at the urban supersite (T0) – Part 1: Fine particle composition and organic source apportionment, Atmos. Chem. Phys., 9, 6633–6653, https://doi.org/10.5194/acp-9-6633-2009, 2009.
Aiken, A. C., de Foy, B., Wiedinmyer, C., DeCarlo, P. F., Ulbrich, I. M., Wehrli, M. N., Szidat, S., Prevot, A. S. H., Noda, J., Wacker, L., Volkamer, R., Fortner, E., Wang, J., Laskin, A., Shutthanandan, V., Zheng, J., Zhang, R., Paredes-Miranda, G., Arnott, W. P., Molina, L. T., Sosa, G., Querol, X., and Jimenez, J. L.: Mexico city aerosol analysis during MILAGRO using high resolution aerosol mass spectrometry at the urban supersite (T0) – Part 2: Analysis of the biomass burning contribution and the non-fossil carbon fraction, Atmos. Chem. Phys., 10, 5315–5341, https://doi.org/10.5194/acp-10-5315-2010, 2010.
Alfarra, M. R., Coe, H., Allan, J. D., Bower, K. N., Boudries, H., Canagaratna, M. R., Jimenez, J. L., Jayne, J. T., Garforth, A. A., Li, S. M., and Worsnop, D. R.: Characterization of Urban and Rural Organic Particulate in the Lower Fraser Valley Using Two Aerodyne Aerosol Mass Spectrometers, Atmos. Environ., 38, 5745–5758, https://doi.org/10.1016/j.atmosenv.2004.01.054, 2004.
Allan, J. D., Jimenez, J. L., Williams, P. I., Alfarra, M. R., Bower, K. N., Jayne, J. T., Coe, H., and Worsnop, D. R.: Quantitative Sampling Using an Aerodyne Aerosol Mass Spectrometer – 1. Techniques of Data Interpretation and Error Analysis, J. Geophys. Res.-Atmos., 108, 4090, https://doi.org/10.1029/2002JD002358, 2003.
Allan, J. D., Delia, A. E., Coe, H., Bower, K. N., Alfarra, M. R., Jimenez, J. L., Middlebrook, A. M., Drewnick, F., Onasch, T. B., Canagaratna, M. R., Jayne, J. T., and Worsnopf, D. R.: A Generalised Method for the Extraction of Chemically Resolved Mass Spectra from Aerodyne Aerosol Mass Spectrometer Data, J. Aerosol Sci., 35, 909–922, https://doi.org/10.1016/j.jaerosci.2004.02.007, 2004.
Allan, J. D., Williams, P. I., Morgan, W. T., Martin, C. L., Flynn, M. J., Lee, J., Nemitz, E., Phillips, G. J., Gallagher, M. W., and Coe, H.: Contributions from transport, solid fuel burning and cooking to primary organic aerosols in two UK cities, Atmos. Chem. Phys., 10, 647–668, https://doi.org/10.5194/acp-10-647-2010, 2010.
Amato, F., Pandolfi, M., Escrig, A., Querol, X., Alastuey, A., Pey, J., Perez, N., and Hopke, P. K.: Quantifying Road Dust Resuspension in Urban Environment by Multilinear Engine: A Comparison with PMF2, Atmos. Environ., 43, 2770–2780, https://doi.org/10.1016/j.atmosenv.2009.02.039, 2009.
Andreae, M. O. and Rosenfeld, D.: Aerosol-Cloud-Precipitation Interactions. Part 1. The Nature and Sources of Cloud-Active Aerosols, Earth-Sci. Rev., 89, 13–41, https://doi.org/10.1016/j.earscirev.2008.03.001, 2008.
Blifford, I. H. and Meeker, G. O.: A Factor Analysis Model of Large Scale Pollution, Atmos. Environ., 1, 147–157, 1967.
Cahill, T. A., Feeney, P. J., and Eldred, R. A.: Size Time Composition Profile of Aerosols Using the DRUM Sampler, Nucl. Instrum. Meth. B, 22, 344–348, 1987.
Canagaratna, M. R., Jayne, J. T., Ghertner, D. A., Herndon, S., Shi, Q., Jimenez, J. L., Silva, P. J., Williams, P., Lanni, T., Drewnick, F., Demerjian, K. L., Kolb, C. E., and Worsnop, D. R.: Chase Studies of Particulate Emissions from in-Use New York City Vehicles, Aerosol Sci. Tech., 38, 555–573, https://doi.org/10.1080/02786820490465504, 2004.
Canagaratna, M. R., Jayne, J. T., Jimenez, J. L., Allan, J. D., Alfarra, M. R., Zhang, Q., Onasch, T. B., Drewnick, F., Coe, H., Middlebrook, A., Delia, A., Williams, L. R., Trimborn, A. M., Northway, M. J., DeCarlo, P. F., Kolb, C. E., Davidovits, P., and Worsnop, D. R.: Chemical and Microphysical Characterization of Ambient Aerosols with the Aerodyne Aerosol Mass Spectrometer, Mass Spectrom. Rev., 26, 185–222, https://doi.org/10.1002/mas.20115, 2007.
Canagaratna, M. R., Onasch, T. B., Wood, E. C., Herndon, S. C., Jayne, J. T., Cross, E. S., Miake-Lye, R. C., Kolb, C. E., and Worsnop, D. R.: Evolution of Vehicle Exhaust Particles in the Atmosphere, J. Air Waste Manage. Assoc., 60, 1192–1203, https://doi.org/10.3155/1047-3289.60.10.1192, 2010.
Chow, J. C. and Watson, J. G.: Review of PM2.5 and PM10 Apportionment for Fossil Fuel Combustion and Other Sources by the Chemical Mass Balance Receptor Model, Energ. Fuel., 16, 222–260, https://doi.org/10.1021/ef0101715, 2002.
Cross, E. S., Onasch, T. B., Canagaratna, M., Jayne, J. T., Kimmel, J., Yu, X.-Y., Alexander, M. L., Worsnop, D. R., and Davidovits, P.: Single particle characterization using a light scattering module coupled to a time-of-flight aerosol mass spectrometer, Atmos. Chem. Phys., 9, 7769–7793, https://doi.org/10.5194/acp-9-7769-2009, 2009.
Cubison, M. J., Ervens, B., Feingold, G., Docherty, K. S., Ulbrich, I. M., Shields, L., Prather, K., Hering, S., and Jimenez, J. L.: The influence of chemical composition and mixing state of Los Angeles urban aerosol on CCN number and cloud properties, Atmos. Chem. Phys., 8, 5649–5667, https://doi.org/10.5194/acp-8-5649-2008, 2008.
DeCarlo, P. F., Kimmel, J. R., Trimborn, A., Northway, M. J., Jayne, J. T., Aiken, A. C., Gonin, M., Fuhrer, K., Horvath, T., Docherty, K. S., Worsnop, D. R., and Jimenez, J. L.: Field-Deployable, High-Resolution, Time-of-Flight Aerosol Mass Spectrometer, Anal. Chem., 78, 8281–8289, https://doi.org/10.1021/ac061249n, 2006.
DeCarlo, P. F., Dunlea, E. J., Kimmel, J. R., Aiken, A. C., Sueper, D., Crounse, J., Wennberg, P. O., Emmons, L., Shinozuka, Y., Clarke, A., Zhou, J., Tomlinson, J., Collins, D. R., Knapp, D., Weinheimer, A. J., Montzka, D. D., Campos, T., and Jimenez, J. L.: Fast airborne aerosol size and chemistry measurements above Mexico City and Central Mexico during the MILAGRO campaign, Atmos. Chem. Phys., 8, 4027–4048, https://doi.org/10.5194/acp-8-4027-2008, 2008.
DeCarlo, P. F., Ulbrich, I. M., Crounse, J., de Foy, B., Dunlea, E. J., Aiken, A. C., Knapp, D., Weinheimer, A. J., Campos, T., Wennberg, P. O., and Jimenez, J. L.: Investigation of the sources and processing of organic aerosol over the Central Mexican Plateau from aircraft measurements during MILAGRO, Atmos. Chem. Phys., 10, 5257–5280, https://doi.org/10.5194/acp-10-5257-2010, 2010.
Dillner, A. M., Schauer, J. J., Christensen, W. F., and Cass, G. R.: A Quantitative Method for Clustering Size Distributions of Elements, Atmos. Environ., 39, 1525–1537, https://doi.org/10.1016/j.atmosenv.2004.11.035, 2005.
Docherty, K. S., Aiken, A. C., Huffman, J. A., Ulbrich, I. M., DeCarlo, P. F., Sueper, D., Worsnop, D. R., Snyder, D. C., Peltier, R. E., Weber, R. J., Grover, B. D., Eatough, D. J., Williams, B. J., Goldstein, A. H., Ziemann, P. J., and Jimenez, J. L.: The 2005 Study of Organic Aerosols at Riverside (SOAR-1): instrumental intercomparisons and fine particle composition, Atmos. Chem. Phys., 11, 12387–12420, https://doi.org/10.5194/acp-11-12387-2011, 2011.
Drewnick, F., Hings, S. S., DeCarlo, P., Jayne, J. T., Gonin, M., Fuhrer, K., Weimer, S., Jimenez, J. L., Demerjian, K. L., Borrmann, S., and Worsnop, D. R.: A New Time-of-Flight Aerosol Mass Spectrometer (TOF-AMS) – Instrument Description and First Field Deployment, Aerosol Sci. Tech., 39, 637–658, https://doi.org/10.1080/02786820500182040, 2005.
Dzepina, K., Volkamer, R. M., Madronich, S., Tulet, P., Ulbrich, I. M., Zhang, Q., Cappa, C. D., Ziemann, P. J., and Jimenez, J. L.: Evaluation of recently-proposed secondary organic aerosol models for a case study in Mexico City, Atmos. Chem. Phys., 9, 5681–5709, https://doi.org/10.5194/acp-9-5681-2009, 2009.
Engel-Cox, J. A. and Weber, S. A.: Compilation and Assessment of Recent Positive Matrix Factorization and Unmix Receptor Model Studies on Fine Particulate Matter Source Apportionment for the Eastern United States, J. Air Waste Manage. Assoc., 57, 1307–1316, https://doi.org/10.3155/1047-3289.57.11.1307, 2007.
Feng, J.: A Size-Resolved Model and a Four-Mode Parameterization of Dry Deposition of Atmospheric Aerosols, J. Geophys. Res.-Atmos., 113, D12201, https://doi.org/10.1029/2007jd009004, 2008.
Gard, E., Mayer, J. E., Morrical, B. D., Dienes, T., Fergenson, D. P., and Prather, K. A.: Real-Time Analysis of Individual Atmospheric Aerosol Particles: Design and Performance of a Portable ATOFMS, Anal. Chem., 69, 4083–4091, 1997.
Gietl, J. K. and Klemm, O.: Source Identification of Size-Segregated Aerosol in Munster, Germany, by Factor Analysis, Aerosol Sci. Tech., 43, 828–837, https://doi.org/10.1080/02786820902953923, 2009.
Gunthe, S. S., King, S. M., Rose, D., Chen, Q., Roldin, P., Farmer, D. K., Jimenez, J. L., Artaxo, P., Andreae, M. O., Martin, S. T., and Pöschl, U.: Cloud condensation nuclei in pristine tropical rainforest air of Amazonia: size-resolved measurements and modeling of atmospheric aerosol composition and CCN activity, Atmos. Chem. Phys., 9, 7551–7575, https://doi.org/10.5194/acp-9-7551-2009, 2009.
Han, J. S., Moon, K. J., Lee, S. J., Kim, Y. J., Ryu, S. Y., Cliff, S. S., and Yi, S. M.: Size-resolved source apportionment of ambient particles by positive matrix factorization at Gosan background site in East Asia, Atmos. Chem. Phys., 6, 211–223, https://doi.org/10.5194/acp-6-211-2006, 2006.
Harshman, R. A. and Lundy, M. E.: PARAFAC – Parallel Factor-Analysis, Comput. Stat. Data An., 18, 39–72, 1994.
Hodzic, A., Jimenez, J. L., Madronich, S., Canagaratna, M. R., DeCarlo, P. F., Kleinman, L., and Fast, J.: Modeling organic aerosols in a megacity: potential contribution of semi-volatile and intermediate volatility primary organic compounds to secondary organic aerosol formation, Atmos. Chem. Phys., 10, 5491–5514, https://doi.org/10.5194/acp-10-5491-2010, 2010.
Huffman, J. A., Docherty, K. S., Aiken, A. C., Cubison, M. J., Ulbrich, I. M., DeCarlo, P. F., Sueper, D., Jayne, J. T., Worsnop, D. R., Ziemann, P. J., and Jimenez, J. L.: Chemically-resolved aerosol volatility measurements from two megacity field studies, Atmos. Chem. Phys., 9, 7161–7182, https://doi.org/10.5194/acp-9-7161-2009, 2009.
Jayne, J. T., Leard, D. C., Zhang, X. F., Davidovits, P., Smith, K. A., Kolb, C. E., and Worsnop, D. R.: Development of an Aerosol Mass Spectrometer for Size and Composition Analysis of Submicron Particles, Aerosol Sci. Tech., 33, 49–70, 2000.
Jimenez, J. L., Jayne, J. T., Shi, Q., Kolb, C. E., Worsnop, D. R., Yourshaw, I., Seinfeld, J. H., Flagan, R. C., Zhang, X. F., Smith, K. A., Morris, J. W., and Davidovits, P.: Ambient Aerosol Sampling Using the Aerodyne Aerosol Mass Spectrometer, J. Geophys. Res.-Atmos., 108, 8425, https://doi.org/10.1029/2001JD001213, 2003.
Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., Prevot, A. S. H., Zhang, Q., Kroll, J. H., DeCarlo, P. F., Allan, J. D., Coe, H., Ng, N. L., Aiken, A. C., Docherty, K. S., Ulbrich, I. M., Grieshop, A. P., Robinson, A. L., Duplissy, J., Smith, J. D., Wilson, K. R., Lanz, V. A., Hueglin, C., Sun, Y. L., Tian, J., Laaksonen, A., Raatikainen, T., Rautiainen, J., Vaattovaara, P., Ehn, M., Kulmala, M., Tomlinson, J. M., Collins, D. R., Cubison, M. J., Dunlea, E. J., Huffman, J. A., Onasch, T. B., Alfarra, M. R., Williams, P. I., Bower, K., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K., Salcedo, D., Cottrell, L., Griffin, R., Takami, A., Miyoshi, T., Hatakeyama, S., Shimono, A., Sun, J. Y., Zhang, Y. M., Dzepina, K., Kimmel, J. R., Sueper, D., Jayne, J. T., Herndon, S. C., Trimborn, A. M., Williams, L. R., Wood, E. C., Middlebrook, A. M., Kolb, C. E., Baltensperger, U., and Worsnop, D. R.: Evolution of Organic Aerosols in the Atmosphere, Science, 326, 1525–1529, https://doi.org/10.1126/science.1180353, 2009.
Karanasiou, A. A., Siskos, P. A., and Eleftheriadis, K.: Assessment of Source Apportionment by Positive Matrix Factorization Analysis on Fine and Coarse Urban Aerosol Size Fractions, Atmos. Environ., 43, 3385–3395, https://doi.org/10.1016/j.atmosenv.2009.03.051, 2009.
Khlystov, A., Zhang, Q., Jimenez, J. L., Stanier, C., Pandis, S. N., Canagaratna, M. R., Fine, P., Misra, C., and Sioutas, C.: In Situ Concentration of Semi-Volatile Aerosol Using Water-Condensation Technology, J. Aerosol Sci., 36, 866–880, https://doi.org/10.1016/j.jaerosci.2004.11.005, 2005.
Kleeman, M. J., Riddle, S. G., Robert, M. A., Jakober, C. A., Fine, P. M., Hays, M. D., Schauer, J. J., and Hannigan, M. P.: Source Apportionment of Fine (PM1.8) and Ultrafine (PM0.1) Airborne Particulate Matter During a Severe Winter Pollution Episode, Environ. Sci. Technol., 43, 272–279, https://doi.org/10.1021/es800400m, 2009.
Lanz, V. A., Alfarra, M. R., Baltensperger, U., Buchmann, B., Hueglin, C., Szidat, S., Wehrli, M. N., Wacker, L., Weimer, S., Caseiro, A., Puxbaum, H., and Prevot, A. S. H.: Source Attribution of Submicron Organic Aerosols During Wintertime Inversions by Advanced Factor Analysis of Aerosol Mass Spectra, Environ. Sci. Technol., 42, 214–220, https://doi.org/10.1021/es0707207, 2008.
Larson, T. V., Covert, D. S., Kim, E., Elleman, R., Schreuder, A. B., and Lumley, T.: Combining Size Distribution and Chemical Species Measurements into a Multivariate Receptor Model of PM2.5, J. Geophys. Res.-Atmos., 111, D10s09, https://doi.org/10.1029/2005jd006285, 2006.
Levin, E. J. T., McMeeking, G. R., Carrico, C. M., Mack, L. E., Kreidenweis, S. M., Wold, C. E., Moosmuller, H., Arnott, W. P., Hao, W. M., Collett, J. L., and Malm, W. C.: Biomass Burning Smoke Aerosol Properties Measured During Fire Laboratory at Missoula Experiments (Flame), J. Geophys. Res.-Atmos., 115, D18210, https://doi.org/10.1029/2009jd013601, 2010.
Liu, P., Ziemann, P. J., Kittelson, D. B., and McMurry, P. H.: Generating Particle Beams of Controlled Dimensions and Divergence, 1. Theory of Particle Motion in Aerodynamic Lenses and Nozzle Expansions, Aerosol Sci. Tech., 22, 293–313, 1995a.
Liu, P., Ziemann, P. J., Kittelson, D. B., and McMurry, P. H.: Generating Particle Beams of Controlled Dimensions and Divergence, 2. Expeimental Evaluation of Particle Motion in Aerodynamic Lenses and Nozzle Expansions, Aerosol Sci. Tech., 22, 314–324, 1995b.
Marple, V. A., Rubow, K. L., and Behm, S. M.: A Microorifice Uniform Deposit Impactor (MOUDI) – Description, Calibration, and Use, Aerosol Sci. Tech., 14, 434–446, 1991.
Mauderly, J. L. and Chow, J. C.: Health Effects of Organic Aerosols, Inhal. Toxicol., 20, 257–288, https://doi.org/10.1080/08958370701866008, 2008.
McLafferty, F. and Turecek, F.: Interpretation of Mass Spectra, 4th Edn., University Science Books, Mill Valley, CA, 1993.
McMeeking, G. R., Kreidenweis, S. M., Carrico, C. M., Lee, T., Collett, J. L., and Malm, W. C.: Observations of Smoke-Influenced Aerosol During the Yosemite Aerosol Characterization Study: Size Distributions and Chemical Composition, J. Geophys. Res.-Atmos., 110, D09206, https://doi.org/10.1029/2004jd005389, 2005.
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.
Molina, L. T., Madronich, S., Gaffney, J. S., Apel, E., de Foy, B., Fast, J., Ferrare, R., Herndon, S., Jimenez, J. L., Lamb, B., Osornio-Vargas, A. R., Russell, P., Schauer, J. J., Stevens, P. S., Volkamer, R., and Zavala, M.: An overview of the MILAGRO 2006 Campaign: Mexico City emissions and their transport and transformation, Atmos. Chem. Phys., 10, 8697–8760, https://doi.org/10.5194/acp-10-8697-2010, 2010.
Murphy, D. M. and Thomson, D. S.: Laser Ionization Mass-Spectroscopy of Single Aerosol-Particles, Aerosol Sci. Tech., 22, 237–249, 1995.
Ng, N. L., Canagaratna, M. R., Jimenez, J. L., Zhang, Q., Ulbrich, I. M., and Worsnop, D. R.: Real-Time Methods for Estimating Organic Component Mass Concentrations from Aerosol Mass Spectrometer Data, Environ. Sci. Technol., 45, 910–916, https://doi.org/10.1021/es102951k, 2011.
Paatero, P.: Least Squares Formulation of Robust Non-Negative Factor Analysis, Chemom. Intell. Lab. Syst., 37, 23–35, 1997a.
Paatero, P.: A Weighted Non-Negative Least Squares Algorithm for Three-Way "PARAFAC" Factor Analysis, Chemom. Intell. Lab. Syst., 38, 223–242, 1997b.
Paatero, P.: The Multilinear Engine – a Table-Driven, Least Squares Program for Solving Multilinear Problems, Including the N-Way Parallel Factor Analysis Model, J. Comput. Graph. Stat., 8, 854–888, 1999.
Paatero, P.: User's Guide for the Multilinear Engine Program "ME2" for Fitting Multilinear and Quasi-Multilinear Models, University of Helsinki, Finland, 2000.
Paatero, P.: User's Guide for Positive Matrix Factorization Programs PMF2.Exe and PMF3.Exe, University of Helsinki, Finland, 2007.
Paatero, P. and Hopke, P. K.: Discarding or Downweighting High-Noise Variables in Factor Analytic Models, Anal. Chim. Acta, 490, 277–289, https://doi.org/10.1016/s0003-2670(02)01643-4, 2003.
Paatero, P. and Hopke, P. K.: Rotational Tools for Factor Analytic Models, J. Chemom., 23, 91–100, https://doi.org/10.1002/cem.1197, 2009.
Paatero, P. and Tapper, U.: Analysis of Different Modes of Factor Analysis as Least Squares Fit Problems, Chemom. Intell. Lab. Syst., 18, 183–194, 1993.
Paatero, P., Hopke, P. K., Song, X. H., and Ramadan, Z.: Understanding and Controlling Rotations in Factor Analytic Models, Chemom. Intell. Lab. Syst., 60, 253–264, 2002.
Pere-Trepat, E., Kim, E., Paatero, P., and Hopke, P. K.: Source Apportionment of Time and Size Resolved Ambient Particulate Matter Measured with a Rotating DRUM Impactor, Atmos. Environ., 41, 5921–5933, https://doi.org/10.1016/j.atmosenv.2007.03.022, 2007.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P.: Statistical Descritption of Data, in: Numerical Recipes: The Art of Scientific Computing, 3rd Edn., Cambridge University Press, New York, USA, 720–772, 2007.
Reff, A., Eberly, S. I., and Bhave, P. V.: Receptor Modeling of Ambient Particulate Matter Data Using Positive Matrix Factorization: Review of Existing Methods, J. Air Waste Manage. Assoc., 57, 146–154, 2007.
Salcedo, D., Onasch, T. B., Aiken, A. C., Williams, L. R., de Foy, B., Cubison, M. J., Worsnop, D. R., Molina, L. T., and Jimenez, J. L.: Determination of particulate lead using aerosol mass spectrometry: MILAGRO/MCMA-2006 observations, Atmos. Chem. Phys., 10, 5371–5389, https://doi.org/10.5194/acp-10-5371-2010, 2010.
Schauer, J. J., Rogge, W. F., Hildemann, L. M., Mazurek, M. A., and Cass, G. R.: Source Apportionment of Airborne Particulate Matter Using Organic Compounds as Tracers, Atmos. Environ., 30, 3837–3855, 1996.
Smith, J. N. and Rathbone, G. J.: Carboxylic Acid Characterization in Nanoparticles by Thermal Desorption Chemical Ionization Mass Spectrometry, Int. J. Mass Spectrom., 274, 8–13, https://doi.org/10.1016/j.ijms.2008.04.008, 2008.
Srivastava, A., Gupta, S., and Jain, V. K.: Winter-Time Size Distribution and Source Apportionment of Total Suspended Particulate Matter and Associated Metals in Delhi, Atmos. Res., 92, 88–99, https://doi.org/10.1016/j.atmosres.2008.09.005, 2009.
Su, Y. X., Sipin, M. F., Furutani, H., and Prather, K. A.: Development and Characterization of an Aerosol Time-of-Flight Mass Spectrometer with Increased Detection Efficiency, Anal. Chem., 76, 712–719, https://doi.org/10.1021/ac034797z, 2004.
Sun, Y.-L., Zhang, Q., Schwab, J. J., Demerjian, K. L., Chen, W.-N., Bae, M.-S., Hung, H.-M., Hogrefe, O., Frank, B., Rattigan, O. V., and Lin, Y.-C.: Characterization of the sources and processes of organic and inorganic aerosols in New York city with a high-resolution time-of-flight aerosol mass apectrometer, Atmos. Chem. Phys., 11, 1581–1602, https://doi.org/10.5194/acp-11-1581-2011, 2011.
Thomson, D. S., Schein, M. E., and Murphy, D. M.: Particle Analysis by Laser Mass Spectrometry Wb-57f Instrument Overview, Aerosol Sci. Tech., 33, 153–169, 2000.
Thurston, G. D. and Spengler, J. D.: A Quantitative Assessment of Source Contributions to Inhalable Particulate Matter Pollution in Metropolitan Boston, Atmos. Environ., 19, 9–25, 1985.
Tobias, H. J., Kooiman, P. M., Docherty, K. S., and Ziemann, P. J.: Real-Time Chemical Analysis of Organic Aerosols Using a Thermal Desorption Particle Beam Mass Spectrometer, Aerosol Sci. Tech., 33, 170–190, 2000.
Tucker, L. R.: Some Mathematical Notes on 3-Mode Factor Analysis, Psychometrika, 31, 279–311, 1966.
Ulbrich, I. M., Canagaratna, M. R., Zhang, Q., Worsnop, D. R., and Jimenez, J. L.: Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data, Atmos. Chem. Phys., 9, 2891–2918, https://doi.org/10.5194/acp-9-2891-2009, 2009.
Viana, M., Kuhlbusch, T. A. J., Querol, X., Alastuey, A., Harrison, R. M., Hopke, P. K., Winiwarter, W., Vallius, A., Szidat, S., Prevot, A. S. H., Hueglin, C., Bloemen, H., Wahlin, P., Vecchi, R., Miranda, A. I., Kasper-Giebl, A., Maenhaut, W., and Hitzenberger, R.: Source Apportionment of Particulate Matter in Europe: A Review of Methods and Results, J. Aerosol Sci., 39, 827–849, https://doi.org/10.1016/j.jaerosci.2008.05.007, 2008.
Wang, J., Cubison, M. J., Aiken, A. C., Jimenez, J. L., and Collins, D. R.: The importance of aerosol mixing state and size-resolved composition on CCN concentration and the variation of the importance with atmospheric aging of aerosols, Atmos. Chem. Phys., 10, 7267–7283, https://doi.org/10.5194/acp-10-7267-2010, 2010.
Watson, J. G.: Visibility: Science and Regulation, J. Air Waste Manage. Assoc., 52, 628–713, 2002.
Whitby, K. T.: Physical Characteristics of Sulfur Aerosols, Atmos. Environ., 12, 135–159, 1978.
Williams, B. J., Goldstein, A. H., Kreisberg, N. M., and Hering, S. V.: An in-Situ Instrument for Speciated Organic Composition of Atmospheric Aerosols: Thermal Desorption Aerosol GC/MS-FID (TAG), Aerosol Sci. Tech., 40, 627–638, https://doi.org/10.1080/02786820600754631, 2006.
Williams, B. J., Goldstein, A. H., Kreisberg, N. M., Hering, S. V., Worsnop, D. R., Ulbrich, I. M., Docherty, K. S., and Jimenez, J. L.: Major components of atmospheric organic aerosol in southern California as determined by hourly measurements of source marker compounds, Atmos. Chem. Phys., 10, 11577–11603, https://doi.org/10.5194/acp-10-11577-2010, 2010.
Yakovleva, E., Hopke, P. K., and Wallace, L.: Receptor Modeling Assessment of Particle Total Exposure Assessment Methodology Data, Environ. Sci. Technol., 33, 3645–3652, 1999.
Yatavelli, R. L. N. and Thornton, J. A.: Particulate Organic Matter Detection Using a Micro-Orifice Volatilization Impactor Coupled to a Chemical Ionization Mass Spectrometer (MOVI-CIMS), Aerosol Sci. Tech., 44, 61–74, https://doi.org/10.1080/02786820903380233, 2010.
Yatkin, S. and Bayram, A.: Source Apportionment of PM10 and PM2.5 Using Positive Matrix Factorization and Chemical Mass Balance in Izmir, Turkey, Sci. Total Environ., 390, 109–123, https://doi.org/10.1016/j.scitotenv.2007.08.059, 2008.
Zelenyuk, A. and Imre, D.: Single Particle Laser Ablation Time-of-Flight Mass Spectrometer: An Introduction to SPLAT, Aerosol Sci. Tech., 39, 554–568, https://doi.org/10.1080/027868291009242, 2005.
Zhang, Q., Worsnop, D. R., Canagaratna, M. R., and Jimenez, J. L.: Hydrocarbon-like and oxygenated organic aerosols in Pittsburgh: insights into sources and processes of organic aerosols, Atmos. Chem. Phys., 5, 3289–3311, https://doi.org/10.5194/acp-5-3289-2005, 2005.
Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Allan, J. D., Coe, H., Ulbrich, I., Alfarra, M. R., Takami, A., Middlebrook, A. M., Sun, Y. L., Dzepina, K., Dunlea, E., Docherty, K., DeCarlo, P., Salcedo, D., Onasch, T. B., Jayne, J. T., Miyoshi, T., Shimono, A., Hatakeyama, N., Takegawa, N., Kondo, Y., Schneider, J., Drewnick, F., Weimer, S., Demerjian, K. L., Williams, P. I., Bower, K. N., Bahreini, R., Cottrell, L., Griffin, R. J., Rautianen, J., and Worsnop, D. R.: Ubiquity and Dominance of Oxygenated Species in Organic Aerosols in Anthropogenically-Influenced Northern Hemisphere Mid-Latitudes, Geophys. Res. Lett., 34, L13801, https://doi.org/10.1029/2007GL029979, 2007.
Zhang, X. F., Smith, K. A., Worsnop, D. R., Jimenez, J. L., Jayne, J. T., Kolb, C. E., Morris, J., and Davidovits, P.: Numerical Characterization of Particle Beam Collimation: Part II – Integrated Aerodynamic-Lens-Nozzle System, Aerosol Sci. Tech., 38, 619–638, doiL10.1080/02786820490479833, 2004.
