Morphology and Internal Mixing of Atmospheric Particles / / edited by Swarup China and Claudio Mazzoleni.

• TeilnehmendeR: China, Swarup,; Mazzoleni, Claudio,
• Place / Publishing House: Basel : : MDPI,, 2018.; ©2018
• Year of Publication: 2018
• Language: English
• Physical Description: 1 online resource (vii, 2014 pages) :; illustrations

The properties of atmospheric particles - often also termed aerosol - have been the subject of scientific studies for several decades because of their effects on air quality, health, visibility, propagation of electromagnetic radiation in the atmosphere, and climate. However, despite intense researc...

Table of Contents

• About the Special Issue Editors . vii
• Swarup China and Claudio Mazzoleni Preface: Morphology and Internal Mixing of Atmospheric Particles Reprinted from: Atmosphere 2018, 9, 249, doi: 10.3390/atmos9070249 . 1
• Chao Chen, Ogochukwu Y. Enekwizu, Yan Ma, Dmitry Zakharov and Alexei F. Khalizov The Impact of Sampling Medium and Environment on Particle Morphology Reprinted from: Atmosphere 2017, 8, 162, doi: 10.3390/atmos8090162 . 7
• Janarjan Bhandari, Swarup China, Timothy Onasch, Lindsay Wolff, Andrew Lambe, Paul Davidovits, Eben Cross, Adam Ahern, Jason Olfert, Manvendra Dubey and Claudio Mazzoleni Effect of Thermodenuding on the Structure of Nascent Flame Soot Aggregates Reprinted from: Atmosphere 2017, 8, 166, doi: 10.3390/atmos8090166 . 24
• Gourihar Kulkarni Immersion Freezing of Total Ambient Aerosols and Ice Residuals Reprinted from: Atmosphere 2018, 9, 55, doi: 10.3390/atmos9020055 . 40
• David Brus, Lenka Skrabalov´ ˇ a, Erik Herrmann, Tinja Olenius, Tereza Tr´avniˇckov´a, Ulla Makkonen and Joonas Merikanto Temperature-Dependent Diffusion of H2SO4 in Air at Atmospherically Relevant Conditions: Laboratory Measurements Using Laminar Flow Technique Reprinted from: Atmosphere 2017, 8, 132, doi: 10.3390/atmos8070132 . 54
• Miho Kiriya, Tomoaki Okuda, Hana Yamazaki, Kazuki Hatoya, Naoki Kaneyasu, Itsushi Uno, Chiharu Nishita, Keiichiro Hara, Masahiko Hayashi, Koji Funato, Kozo Inoue, Shigekazu Yamamoto, Ayako Yoshino and Akinori Takami Monthly and Diurnal Variation of the Concentrations of Aerosol Surface Area in Fukuoka, Japan, Measured by Diffusion Charging Method Reprinted from: Atmosphere 2017, 8, 114, doi: 10.3390/atmos8070114 . 69
• Manasi Mahish, Anne Jefferson and Don R. Collins Influence of Common Assumptions Regarding Aerosol Composition and Mixing State on Predicted CCN Concentration Reprinted from: Atmosphere 2018, 9, 54, doi: 10.3390/atmos9020054 . 83
• Liang Xu, Lei Liu, Jian Zhang, Yinxiao Zhang, Yong Ren, Xin Wang and Weijun Li Morphology, Composition, and Mixing State of Individual Aerosol Particles in Northeast China during Wintertime Reprinted from: Atmosphere 2017, 8, 47, doi: 10.3390/atmos8030047 . 101
• Wenhua Wang, Longyi Shao, Jiaoping Xing, Jie Li, Lingli Chang and Wenjun Li Physicochemical Characteristics of Individual Aerosol Particles during the 2015 China Victory Day Parade in Beijing Reprinted from: Atmosphere 2018, 9, 40, doi: 10.3390/atmos9020040 . 111
• Matthew Fraund, Don Q. Pham, Daniel Bonanno, Tristan H. Harder, Bingbing Wang, Joel Brito, Suzane S. de S´a, Samara Carbone, Swarup China, Paulo Artaxo, Scot T. Martin, Christopher P ¨ohlker, Meinrat O. Andreae, Alexander Laskin, Mary K. Gilles and Ryan C. Moffet Elemental Mixing State of Aerosol Particles Collected in Central Amazonia during GoAmazon2014/15 Reprinted from: Atmosphere 2017, 8, 173, doi: 10.3390/atmos8090173 . 121
• Christopher M. Sorensen, Yuli W. Heinson, William R. Heinson, Justin B. Maughan and Amit Chakrabarti Q-Space Analysis of the Light Scattering Phase Function of Particles with Any Shape Reprinted from: Atmosphere 2017, 8, 68, doi: 10.3390/atmos8040068 . 149
• Joseph Ching, Matthew West, Nicole Riemer Quantifying Impacts of Aerosol Mixing State on Nucleation-Scavenging of Black Carbon Aerosol Particles Reprinted from: Atmosphere 2018, 9, 17, doi: 10.3390/atmos9010017 . 170
• Michael Hughes, John K. Kodros, Jeffrey R. Pierce, Matthew West and Nicole Riemer Machine Learning to Predict the Global Distribution of AerosolMixing State Metrics Reprinted from: Atmosphere 2018, 9, 15, doi: 10.3390/atmos9010015 . 187.