Articles | Volume 8, issue 8
https://doi.org/10.5194/amt-8-3263-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-8-3263-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
13 Aug 2015
Research article |
| 13 Aug 2015
Plume-based analysis of vehicle fleet air pollutant emissions and the contribution from high emitters
Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S3E5, Canada
C.-H. Jeong
Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S3E5, Canada
N. Zimmerman
Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S3E5, Canada
R. M. Healy
Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S3E5, Canada
D. K. Wang
Analysis and Air Quality Section, Air Quality Research Division, Environment Canada, Ottawa, Ontario K1V1C7, Canada
F. Ke
Analysis and Air Quality Section, Air Quality Research Division, Environment Canada, Ottawa, Ontario K1V1C7, Canada
G. J. Evans
Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S3E5, Canada
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Single-particle measurements from a soot-particle aerosol mass spectrometer were performed to examine the mixing state of aerosol particles in an air mass influenced by aged biomass burning. Our observations indicate non-uniform mixing of particles within a biomass burning plume in terms of molecular weight and potassium content, and illustrate that high molecular weight organic compounds can be a key contributor to low-volatility BrC observed in biomass burning organic aerosols.
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The source identification and quantification analysis of hourly resolved particulate matter chemical speciation data in downtown Toronto, Canada offered many advantages in terms of the identification of more sources and the resolution of more robust factor profiles and contributions. The results provide additional insight into sources of fine particle pollutants that have high temporal variations and thereby support the development of more effective control strategies for ambient pollutants.
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Increased interest in monitoring air quality near roadways, combined with traffic's often unclear contribution to elevated concentrations, has created a need for better interpretation of these data. Using 2 years of measurements collected during a near-road monitoring project in Canada, this paper contrasts three methods for estimating the fraction of roadside pollution resulting from on-road traffic. Robustness of these methods was compared with tandem measurements at background locations.
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The sources of PM2.5 components exhibited short-term variabilities, and their contributions were identified in the Athabasca oil sands region. Much of the trace elements were found to originate from anthropogenic activities, i.e., oil sands upgrading and on- and off-road transportation. Some of these anthropogenic activities became better defined and understood only through highly time-resolved measurements, which can help guide further studies and policy decisions in the oil sands area.
Katrina M. Macdonald, Sangeeta Sharma, Desiree Toom, Alina Chivulescu, Sarah Hanna, Allan K. Bertram, Andrew Platt, Mike Elsasser, Lin Huang, David Tarasick, Nathan Chellman, Joseph R. McConnell, Heiko Bozem, Daniel Kunkel, Ying Duan Lei, Greg J. Evans, and Jonathan P. D. Abbatt
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Rapid climate changes within the Arctic have highlighted existing uncertainties in the transport of contaminants to Arctic snow. Fresh snow samples collected frequently through the winter season were analyzed for major constituents creating a unique record of Arctic snow. Comparison with simultaneous atmospheric measurements provides insight into the driving processes in the transfer of contaminants from air to snow. The relative importance of deposition mechanisms over the season is proposed.
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Cheol-Heon Jeong, Jon M. Wang, and Greg J. Evans
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-189, https://doi.org/10.5194/acp-2016-189, 2016
Revised manuscript not accepted
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A. K. Y. Lee, M. D. Willis, R. M. Healy, T. B. Onasch, and J. P. D. Abbatt
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M. L. McGuire, R. Y.-W. Chang, J. G. Slowik, C.-H. Jeong, R. M. Healy, G. Lu, C. Mihele, J. P. D. Abbatt, J. R. Brook, and G. J. Evans
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R. M. Healy, N. Riemer, J. C. Wenger, M. Murphy, M. West, L. Poulain, A. Wiedensohler, I. P. O'Connor, E. McGillicuddy, J. R. Sodeau, and G. J. Evans
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D. Mendolia, R. J. C. D'Souza, G. J. Evans, and J. Brook
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Data imputation in in situ-measured particle size distributions by means of neural networks
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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
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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
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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
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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
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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
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Christina Vasilakopoulou, Iasonas Stavroulas, Nikolaos Mihalopoulos, and Spyros N. Pandis
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Anton Rusanen, Kristo Hõrrak, Lauri R. Ahonen, Tuomo Nieminen, Pasi P. Aalto, Pasi Kolari, Markku Kulmala, Tuukka Petäjä, and Heikki Junninen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-67, https://doi.org/10.5194/amt-2022-67, 2022
Revised manuscript accepted for AMT
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We present a framework for setting up SMEAR (Station for Measuring Earth Surface-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, real-time monitoring of measurement results and station status.
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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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.
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.
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.
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.
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.
Cited articles
Araizaga, A. E., Mancilla, Y., and Mendoza, A.: Volatile Organic Compound Emissions from Light-Duty Vehicles in Monterrey, Mexico: a Tunnel Study, Int. J. Environ. Res., 7, 277–292, 2013.
Ban-Weiss, G. A., Lunden, M. M., Kirchstetter, T. W., and Harley, R. A.: Measurement of Black Carbon and Particle Number Emission Factors from Individual Heavy-Duty Trucks, Environ. Sci. Technol., 43, 1419–1424, https://doi.org/10.1021/es8021039, 2009.
Ban-Weiss, G. A., Lunden, M. M., Kirchstetter, T. W., and Harley, R. A.: Size-resolved particle number and volume emission factors for on-road gasoline and diesel motor vehicles, J. Aerosol Sci., 41, 5–12, https://doi.org/10.1016/j.jaerosci.2009.08.001, 2010.
Barth, M., An, F., Younglove, T., Levine, C., Socora, G., Ross, M., and Wenzel, T.: Development of a Comprehensive Modal Emissions Model – Final Report NCHRP Project 25-11, Transportation Research Board – National Research Council, Washington DC, 1–214, 1999.
Bishop, G. A. and Stedman, D. H.: Measuring the emissions of passing cars, Acc. Chem. Res., 29, 489–495, 1996.
Bishop, G. A., Stedman, D. H., and Ashbaugh, L.: Motor Vehicle Emissions Variability, J. Air. Waste Manage., 46, 667–675, https://doi.org/10.1080/10473289.1996.10467501, 1996.
Bishop, G. A. and Stedman, D. H.: A Decade of On-road Emissions Measurements, Environ. Sci. Technol., 42, 1651–1656, https://doi.org/10.1021/es702413b, 2008.
Bishop, G. A., Schuchmann, B. G., Stedman, D. H., and Lawson, D. R.: Emission Changes Resulting from the San Pedro Bay, California Ports Truck Retirement Program, Environ. Sci. Technol., 46, 551–558, https://doi.org/10.1021/es202392g, 2011.
Chan, T. L., Ning, Z., Leung, C. W., Cheung, C. S., Hung, W. T., and Dong, G.: On-road remote sensing of petrol vehicle emissions measurement and emission factors estimation in Hong Kong, Atmos. Environ., 38, 2055–2066, https://doi.org/10.1016/j.atmosenv.2004.01.031, 2004.
Dallmann, T. R. and Harley, R. A.: Evaluation of mobile source emission trends in the United States, J. Geophys. Res.-Atmos., 115, D14305, https://doi.org/10.1029/2010JD013862, 2010.
Dallmann, T. R., DeMartini, S. J., Kirchstetter, T. W., Herndon, S. C., Onasch, T. B., Wood, E. C., and Harley, R. A.: On-Road Measurement of Gas and Particle Phase Pollutant Emission Factors for Individual Heavy-Duty Diesel Trucks, Environ. Sci. Technol., 46, 8511–8518, https://doi.org/10.1021/es301936c, 2012.
Dallmann, T. R., Kirchstetter, T. W., DeMartini, S. J., and Harley, R. A.: Quantifying On-Road Emissions from Gasoline-Powered Motor Vehicles: Accounting for the Presence of Medium- and Heavy-Duty Diesel Trucks, Environ. Sci. Technol., 47, 13873–13881, https://doi.org/10.1021/es402875u, 2013.
DeWitt, H. L., Hellebust, S., Temime-Roussel, B., Ravier, S., Polo, L., Jacob, V., Buisson, C., Charron, A., André, M., Pasquier, A., Besombes, J. L., Jaffrezo, J. L., Wortham, H., and Marchand, N.: Near-highway aerosol and gas-phase measurements in a high-diesel environment, Atmos. Chem. Phys., 15, 4373–4387, https://doi.org/10.5194/acp-15-4373-2015, 2015.
EC: Uncertainty Analysis of Criteria Air Contaminants from Mobile Sources in Canada, Environment Canada, Ottawa, Canada, 1–4, 2008.
Evans, G. J., Jeong, C.-H., Sabaliauskas, K., Jadidian, P., Aldersley, S., Larocque, H., and Herod, D.: Design of a Near-Road Monitoring Strategy for Canada, A Final Report to Environment Canada, SOCAAR, Toronto, 1–60, 2011.
Franco, V., Kousoulidou, M., Muntean, M., Ntziachristos, L., Hausberger, S., and Dilara, P.: Road vehicle emission factors development: A review, Atmos. Environ., 70, 84–97, https://doi.org/10.1016/j.atmosenv.2013.01.006, 2013.
Geller, M. D., Sardar, S. B., Phuleria, H., Fine, P. M., and Sioutas, C.: Measurements of Particle Number and Mass Concentrations and Size Distributions in a Tunnel Environment, Environ. Sci. Technol., 39, 8653–8663, https://doi.org/10.1021/es050360s, 2005.
Gentner, D. R., Worton, D. R., Isaacman, G., Davis, L. C., Dallmann, T. R., Wood, E. C., Herndon, S. C., Goldstein, A. H., and Harley, R. A.: Chemical Composition of Gas-Phase Organic Carbon Emissions from Motor Vehicles and Implications for Ozone Production, Environ. Sci. Technol., 47, 11837–11848, https://doi.org/10.1021/es401470e, 2013.
Gertler, A. W.: Diesel vs. gasoline emissions: Does PM from diesel or gasoline vehicles dominate in the US?, Atmos. Environ., 39, 2349–2355, https://doi.org/10.1016/j.atmosenv.2004.05.065, 2005.
Guo, H., Zhang, Q., Shi, Y., and Wang, D.: On-road remote sensing measurements and fuel-based motor vehicle emission inventory in Hangzhou, China, Atmos. Environ., 41, 3095–3107, https://doi.org/10.1016/j.atmosenv.2006.11.045, 2007.
Hansen, A. D. A. and Rosen, H.: Individual Measurements of the Emission Factor of Aerosol Black Carbon in Automobile Plumes, J. Air. Waste Manage., 40, 1654–1657, https://doi.org/10.1080/10473289.1990.10466812, 1990.
HEI: Traffic-Related Air Pollution: A Critical Review of the Literature on Emissions, Exposure, and Health Effects Health Effects Institute, Boston, 1–108, 2010.
Ho, K. F., Lee, S. C., Ho, W. K., Blake, D. R., Cheng, Y., Li, Y. S., Ho, S. S. H., Fung, K., Louie, P. K. K., and Park, D.: Vehicular emission of volatile organic compounds (VOCs) from a tunnel study in Hong Kong, Atmos. Chem. Phys., 9, 7491–7504, https://doi.org/10.5194/acp-9-7491-2009, 2009.
Hu, J., Wu, Y., Wang, Z., Li, Z., Zhou, Y., Wang, H., Bao, X., and Hao, J.: Real-world fuel efficiency and exhaust emissions of light-duty diesel vehicles and their correlation with road conditions, J. Environ. Sci., 24, 865–874, https://doi.org/10.1016/S1001-0742(11)60878-4, 2012.
Hudda, N., Fruin, S., Delfino, R. J., and Sioutas, C.: Efficient determination of vehicle emission factors by fuel use category using on-road measurements: downward trends on Los Angeles freight corridor I-710, Atmos. Chem. Phys., 13, 347–357, https://doi.org/10.5194/acp-13-347-2013, 2013.
Hwa, M.-Y., Hsieh, C.-C., Wu, T.-C., and Chang, L.-F. W.: Real-world vehicle emissions and VOCs profile in the Taipei tunnel located at Taiwan Taipei area, Atmos. Environ., 36, 1993–2002, https://doi.org/10.1016/S1352-2310(02)00148-6, 2002.
Imhof, D., Weingartner, E., Ordóñez, C., Gehrig, R., Hill, M., Buchmann, B., and Baltensperger, U.: Real-World Emission Factors of Fine and Ultrafine Aerosol Particles for Different Traffic Situations in Switzerland, Environ. Sci. Technol., 39, 8341–8350, https://doi.org/10.1021/es048925s, 2005.
Janhall, S., Molnar, P., and Hallquist, M.: Traffic emission factors of ultrafine particles: effects from ambient air, J. Environ. Monit., 14, 2488–2496, https://doi.org/10.1039/C2EM30235G, 2012.
Jeong, C.-H., Hopke, P. K., Kim, E., and Lee, D.-W.: The comparison between thermal-optical transmittance elemental carbon and Aethalometer black carbon measured at multiple monitoring sites, Atmos. Environ., 38, 5193–5204, https://doi.org/10.1016/j.atmosenv.2004.02.065, 2004.
Jeong, C.-H., McGuire, M. L., Godri, K. J., Slowik, J. G., Rehbein, P. J. G., and Evans, G. J.: Quantification of aerosol chemical composition using continuous single particle measurements, Atmos. Chem. Phys., 11, 7027–7044, https://doi.org/10.5194/acp-11-7027-2011, 2011.
Ježek, I., Drinovec, L., Ferrero, L., Carriero, M., and Mocnik, G.: Determination of car on-road black carbon and particle number emission factors and comparison between mobile and stationary measurements, Atmos. Meas. Tech. Discuss., 7, 5423–5455, https://doi.org/10.5194/amtd-7-5423-2014, 2014.
Jimenez, J. L., McManus, J. B., Shorter, J. H., Nelson, D. D., Zahniser, M. S., Koplow, M., McRae, G. J., and Kolb, C. E.: Cross road and mobile tunable infrared laser measurements of nitrous oxide emissions from motor vehicles, Chemosphere – Global Change Science, 2, 397–412, https://doi.org/10.1016/S1465-9972(00)00019-2, 2000.
Jordan, A., Haidacher, S., Hanel, G., Hartungen, E., Märk, L., Seehauser, H., Schottkowsky, R., Sulzer, P., and Märk, T. D.: A high resolution and high sensitivity proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS), Int. J. Mass Spectrom., 286, 122–128, https://doi.org/10.1016/j.ijms.2009.07.005, 2009.
Kalafut-Pettibone, A. J., Wang, J., Eichinger, W. E., Clarke, A., Vay, S. A., Blake, D. R., and Stanier, C. O.: Size-resolved aerosol emission factors and new particle formation/growth activity occurring in Mexico City during the MILAGRO 2006 Campaign, Atmos. Chem. Phys., 11, 8861–8881, https://doi.org/10.5194/acp-11-8861-2011, 2011.
Kampa, M. and Castanas, E.: Human health effects of air pollution, Environ. Pollut., 151, 362–367, https://doi.org/10.1016/j.envpol.2007.06.012, 2008.
Kean, A. J., Harley, R. A., and Kendall, G. R.: Effects of Vehicle Speed and Engine Load on Motor Vehicle Emissions, Environ. Sci. Technol., 37, 3739–3746, https://doi.org/10.1021/es0263588, 2003.
Kirchstetter, T. W., Harley, R. A., Kreisberg, N. M., Stolzenburg, M. R., and Hering, S. V.: On-road measurement of fine particle and nitrogen oxide emissions from light- and heavy-duty motor vehicles, Atmos. Environ., 33, 2955–2968, https://doi.org/10.1016/S1352-2310(99)00089-8, 1999.
Ko, Y.-W. and Cho, C.-H.: Characterization of large fleets of vehicle exhaust emissions in middle Taiwan by remote sensing, Sci. Total Environ., 354, 75–82, https://doi.org/10.1016/j.scitotenv.2005.05.040, 2006.
Kristensson, A., Johansson, C., Westerholm, R., Swietlicki, E., Gidhagen, L., Wideqvist, U., and Vesely, V.: Real-world traffic emission factors of gases and particles measured in a road tunnel in Stockholm, Sweden, Atmos. Environ., 38, 657–673, https://doi.org/10.1016/j.atmosenv.2003.10.030, 2004.
Kuhns, H. D., Mazzoleni, C., Moosmüller, H., Nikolic, D., Keislar, R. E., Barber, P. W., Li, Z., Etyemezian, V., and Watson, J. G.: Remote sensing of PM, NO, CO and HC emission factors for on-road gasoline and diesel engine vehicles in Las Vegas, NV, Sci. Total Environ., 322, 123–137, https://doi.org/10.1016/j.scitotenv.2003.09.013, 2004.
Laden, F., Schwartz, J., Speizer, F. E., and Dockery, D. W.: Reduction in Fine Particulate Air Pollution and Mortality, American Journal of Respiratory and Critical Care Medicine, 173, 667–672, https://doi.org/10.1164/rccm.200503-443OC, 2006.
Lawson, D. R., Groblicki, P. J., Stedman, D. H., Bishop, G. A., and Guenther, P. L.: Emissions from lit-use Motor Vehicles in Los Angeles: A Pilot Study of Remote Sensing and the Inspection and Maintenance Program, J. Air. Waste Manage., 40, 1096–1105, https://doi.org/10.1080/10473289.1990.10466754, 1990.
Liggio, J., Gordon, M., Smallwood, G., Li, S.-M., Stroud, C., Staebler, R., Lu, G., Lee, P., Taylor, B., and Brook, J. R.: Are Emissions of Black Carbon from Gasoline Vehicles Underestimated? Insights from Near and On-Road Measurements, Environ. Sci. Technol., 46, 4819–4828, https://doi.org/10.1021/es2033845, 2012.
Mancilla, Y. and Mendoza, A.: A tunnel study to characterize PM2.5 emissions from gasoline-powered vehicles in Monterrey, Mexico, Atmos. Environ., 59, 449–460, https://doi.org/10.1016/j.atmosenv.2012.05.025, 2012.
May, A. A., Presto, A. A., Hennigan, C. J., Nguyen, N. T., Gordon, T. D., and Robinson, A. L.: Gas-particle partitioning of primary organic aerosol emissions: (1) Gasoline vehicle exhaust, Atmos. Environ., 77, 128–139, https://doi.org/10.1016/j.atmosenv.2013.04.060, 2013.
May, A. A., Nguyen, N. T., Presto, A. A., Gordon, T. D., Lipsky, E. M., Karve, M., Gutierrez, A., Robertson, W. H., Zhang, M., Brandow, C., Chang, O., Chen, S., Cicero-Fernandez, P., Dinkins, L., Fuentes, M., Huang, S.-M., Ling, R., Long, J., Maddox, C., Massetti, J., McCauley, E., Miguel, A., Na, K., Ong, R., Pang, Y., Rieger, P., Sax, T., Truong, T., Vo, T., Chattopadhyay, S., Maldonado, H., Maricq, M. M., and Robinson, A. L.: Gas- and particle-phase primary emissions from in-use, on-road gasoline and diesel vehicles, Atmos. Environ., 88, 247–260, https://doi.org/10.1016/j.atmosenv.2014.01.046, 2014.
McCarter, J.: 2012 Annual Report, Office of the Auditor General of Ontario, Toronto, Canada, 107–128, 2012.
Miguel, A. H., Kirchstetter, T. W., Harley, R. A., and Hering, S. V.: On-Road Emissions of Particulate Polycyclic Aromatic Hydrocarbons and Black Carbon from Gasoline and Diesel Vehicles, Environ. Sci. Technol., 32, 450–455, https://doi.org/10.1021/es970566w, 1998.
MoE: Drive Clean Guide – Emissions Standards, Emission Test Methods, and Technical Information Relating to Ontario Regulation 361/98 As Amended, Ontario Ministry of the Environment, Toronto, Ontario, 1–20, 2010.
Nickel, C., Kaminski, H., Bellack, B., Quass, U., John, A., Klemm, O., and Kuhlbusch, T. A. J.: Size Resolved Particle Number Emission Factors of Motorway Traffic Differentiated between Heavy and Light Duty Vehicles, Aerosol Air Qual. Res., 13, 450–461, 2013.
Ning, Z., Polidori, A., Schauer, J. J., and Sioutas, C.: Emission factors of PM species based on freeway measurements and comparison with tunnel and dynamometer studies, Atmos. Environ., 42, 3099–3114, https://doi.org/10.1016/j.atmosenv.2007.12.039, 2008.
Park, S. S., Kozawa, K., Fruin, S., Mara, S., Hsu, Y.-K., Jakober, C., Winer, A., and Herner, J.: Emission Factors for High-Emitting Vehicles Based on On-Road Measurements of Individual Vehicle Exhaust with a Mobile Measurement Platform, J. Air. Waste Manage., 61, 1046–1056, https://doi.org/10.1080/10473289.2011.595981, 2011.
Phuleria, H. C., Sheesley, R. J., Schauer, J. J., Fine, P. M., and Sioutas, C.: Roadside measurements of size-segregated particulate organic compounds near gasoline and diesel-dominated freeways in Los Angeles, CA, Atmos. Environ., 41, 4653–4671, https://doi.org/10.1016/j.atmosenv.2007.03.031, 2007.
Pierson, W. R. and Brachaczek, W. W.: Particulate Matter Associated with Vehicles on the Road. II, Aerosol Sci. Technol., 2, 1–40, https://doi.org/10.1080/02786828308958610, 1982.
Rehbein, P. J. G., Jeong, C.-H., McGuire, M. L., and Evans, G. J.: Strategies to Enhance the Interpretation of Single-Particle Ambient Aerosol Data, Aerosol Sci. Technol., 46, 584–595, https://doi.org/10.1080/02786826.2011.650334, 2011.
Rogak, S. N., Pott, U., Dann, T., and Wang, D.: Gaseous Emissions from Vehicles in a Traffic Tunnel in Vancouver, British Columbia, J. Air. Waste Manage., 48, 604–615, https://doi.org/10.1080/10473289.1998.10463713, 1998.
Sabaliauskas, K., Jeong, C.-H., Yao, X., Jun, Y.-S., Jadidian, P., and Evans, G. J.: Five-year roadside measurements of ultrafine particles in a major Canadian city, Atmos. Environ., 49, 245–256, https://doi.org/10.1016/j.atmosenv.2011.11.052, 2012.
Sadler, L., Jenkins, N., Legassick, W., and Sokhi, R. S.: Remote sensing of vehicle emissions on British urban roads, Sci. Total Environ., 189–190, 155–160, https://doi.org/10.1016/0048-9697(96)05204-7, 1996.
Schwartz, J.: Smog Check II Evaluation – Part II: Overview of Vehicle Emissions, California Inspection and Maintenance Review Committee, Sacramento, California, 1–72, 2000.
Seinfeld, J. H. and Pandis, S. N.: Organic Atmospheric Aerosols, in: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, John Wiley & Sons Inc., Hoboken, New Jersey, 628–657, 2006.
Sims, R., Schaeffer, R., Creutzig, F., Cruz-Núñez, X., D'Agosto, M., Dimitriu, D., Meza, M. J. F., Fulton, L., Kobayashi, S., Lah, O., McKinnon, A., Newman, P., Ouyang, M., Schauer, J. J., Sperling, D., and Tiwari, G.: Transporation, Intergovernmental Panel on Climate Change, Cambridge, United Kingdom, 599–670, 2014.
StatsCan: Canadian Vehicle Survey: Annual 2009, Statistics Canada, Ottawa, Ontario, 1–41, 2010.
Stephens, R. D.: Remote Sensing Data and a Potential Model of Vehicle Exhaust Emissions, J. Air. Waste Manage., 44, 1284–1294, 1994.
USEPA: Average In-Use Emissions from Heavy-Duty Trucks, United States Environmental Protection Agency, Washington DC, United States, 6, 1–6, 2008a.
USEPA: Average Annual Emissions and Fuel Consumption for Gasoline-Fueled Passenger Cars and Light Trucks, United States Environmental Protection Agency, Washington DC, United States, 6, 1–6, 2008b.
USEPA: Technical Support Document, Preparation of Emissions Inventories for the Version 6.0, 2011 Emissions Modeling Platform, US Environmental Protection Agency, Washington DC, United States, 1–146, 2013.
Wang, F., Ketzel, M., Ellermann, T., Wåhlin, P., Jensen, S. S., Fang, D., and Massling, A.: Particle number, particle mass and NOx emission factors at a highway and an urban street in Copenhagen, Atmos. Chem. Phys., 10, 2745–2764, https://doi.org/10.5194/acp-10-2745-2010, 2010.
Wang, X., Westerdahl, D., Chen, L. C., Wu, Y., Hao, J., Pan, X., Guo, X., and Zhang, K. M.: Evaluating the air quality impacts of the 2008 Beijing Olympic Games: On-road emission factors and black carbon profiles, Atmos. Environ., 43, 4535–4543, https://doi.org/10.1016/j.atmosenv.2009.06.054, 2009.
Weinstock, L., Watkins, N., Wayland, R., and Baldauf, R.: EPA's Emerging Near-Road Ambient Monitoring Network: A Progress Report, Air & Waste Management Association, Pittsburg, Pennsylvania, 6–10, 2013.
Wenzel, T., Singer, B. C., and Slott, R.: Some Issues in the Statistical Analysis of Vehicle Emissions, Journal of Transportation and Statistics, 3, 1–14, 2000.
Westerdahl, D., Wang, X., Pan, X., and Zhang, K. M.: Characterization of on-road vehicle emission factors and microenvironmental air quality in Beijing, China, Atmos. Environ., 43, 697–705, https://doi.org/10.1016/j.atmosenv.2008.09.042, 2009.
WHO: 7 million premature deaths annually linked to air pollution, World Health Organization, Geneva, Switzerland, 2014.
