Severe haze events with exceedingly high-levels of fine aerosols occur frequently over the past decades in the North China Plain (NCP), exerting profound impacts on human health, weather, and climate. The development of effective mitigation policies requires a comprehensive understanding of the haze formation mechanisms, including identification and quantification of the sources, formation, and transformation of the aerosol species. Haze evolution in this region exhibits distinct physical and chemical characteristics from clean to polluted periods, as evident from increasing stagnation and relative humidity, but decreasing solar radiation as well as explosive secondary aerosol formation. The latter is attributed to highly elevated concentrations of aerosol precursor gases and is reflected by rapid increases in the particle number and mass concentrations, both corresponding to nonequilibrium chemical processes. Considerable new knowledge has been acquired to understand the processes regulating haze formation, particularly in light of the progress in elucidating the aerosol formation mechanisms. This review synthesizes recent advances in understanding secondary aerosol formation, by highlighting several critical chemical/physical processes, that is, new particle formation and aerosol growth driven by photochemistry and aqueous chemistry as well as the interaction between aerosols and atmospheric stability. Current challenges and future research priorities are also discussed.

Explosive Secondary Aerosol Formation during Severe Haze in the North China Plain.

Overview of measurements and current instrumentation for 1–10 nm aerosol particle number size distributions

Intense and frequent new particle formation (NPF) events have been observed in polluted urban environments, yet the dominant mechanisms are still under debate. To understand the key species and governing processes of NPF in polluted urban environments, we conducted comprehensive measurements in downtown Beijing during January–March, 2018. We performed detailed analyses on sulfuric acid cluster composition and budget, as well as the chemical and physical properties of oxidized organic molecules (OOMs). Our results demonstrate that the fast clustering of sulfuric acid (H2SO4) and base molecules triggered the NPF events, and OOMs further helped grow the newly formed particles toward climateand health-relevant sizes. This synergistic role of H2SO4, base species, and OOMs in NPF is likely representative of polluted urban environments where abundant H2SO4 and base species usually co-exist, and OOMs are with moderately low volatility when produced under high NOx concentrations. Plain Language Summary Atmospheric new particle formation (NPF) is a dominant source of atmospheric ultrafine particles worldwide. Those particles profoundly influence climate and human health. NPF includes two consecutive processes, that is, the formation of new particles (∼2 nm in diameter) and their subsequent growth to larger sizes. Extensive studies conducted in the laboratory and in forested areas have shown that many gaseous species can participate in NPF, such as sulfuric acid, ammonia, amines, and oxidize organic molecules. However, the actual roles of these vapors may vary significantly from location to location and are largely unclear in urban environments. Here, based on measurements of sulfuric acid, sulfuric acid clusters, and oxidize organic molecules, we demonstrate that sulfuric acid and base molecules were responsible for the initial formation of new particles during a wintertime field campaign in Beijing. The majority of oxidized organic molecules had a minor YAN ET AL. © 2021. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. The Synergistic Role of Sulfuric Acid, Bases, and Oxidized Organics Governing New-Particle Formation in Beijing Chao Yan , Rujing Yin, Yiqun Lu , Lubna Dada , Dongsen Yang, Yueyun Fu, Jenni Kontkanen , Chenjuan Deng, Olga Garmash , Jiaxin Ruan, Rima Baalbaki , Meredith Schervish, Runlong Cai, Matthew Bloss, Tommy Chan , Tianzeng Chen , Qi Chen, Xuemeng Chen, Yan Chen, Biwu Chu, Kaspar Dällenbach, Benjamin Foreback, Xucheng He, Liine Heikkinen , Tuija Jokinen, Heikki Junninen, Juha Kangasluoma , Tom Kokkonen , Mona Kurppa, Katrianne Lehtipalo , Haiyan Li, Hui Li , Xiaoxiao Li, Yiliang Liu, Qingxin Ma , Pauli Paasonen , Pekka Rantala , Rosaria E. Pileci , Anton Rusanen, Nina Sarnela , Pauli Simonen , Shixian Wang, Weigang Wang , Yonghong Wang , Mo Xue, Gan Yang, Lei Yao, Ying Zhou, Joni Kujansuu, Tuukka Petäjä, Wei Nie , Yan Ma, Maofa Ge , Hong He, Neil M. Donahue , Douglas R. Worsnop, Veli-Matti Kerminen, Lin Wang , Yongchun Liu, Jun Zheng , Markku Kulmala , Jingkun Jiang , and Federico Bianchi Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Aerosol and Haze Laboratory, Beijing University of Chemical Technology, Beijing, China, Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland, School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, China, Department of Environmental Science & Engineering, Fudan University, Shanghai, China, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, Nanjing, China, Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, USA, Research Center for EcoEnvironmental Sciences, Chinese Academy of Science, Beijing, China, School of Environmental Sciences, Peking University, Beijing, China, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China, Laboratory of Environmental Physics, Institute of Physics, University of Tartu, Tartu, Estonia, Finnish Meteorological Institute, Helsinki, Finland, Laboratory of Atmospheric Chemistry, Paul Scherrer Institute (PSI), Villigen, Switzerland, Aerosol Physics Laboratory, Physics Unit, Tampere University, Tampere, Finland, School of Atmospheric Sciences, Joint International Research Laboratory of Atmospheric and Earth System Sciences, Nanjing University, Nanjing, China, Aerodyne Research Inc., Billerica, MA, USA Key Points: • Process-level understanding of new particle formation (NPF) in wintertime Beijing was obtained based on measurement state-of-theart instruments • The analysis of sulfuric acid cluster composition and budget showed that sulfuric acid-base clustering initiated NPF • Condensable organic vapors were characterized and demonstrated to have a crucial influence on the growth of newly formed particles Supporting Information: Supporting Information may be found in the online version of this article.

The Synergistic Role of Sulfuric Acid, Bases, and Oxidized Organics Governing New-Particle Formation in Beijing

. The spatial and temporal variability of the number size distribution of
aerosol particles is an indicator of the dynamic behavior of Beijing's
atmospheric pollution cocktail. This variation reflects the strength of different
primary and secondary sources, such as traffic and new particle formation,
as well as the main processes affecting the particle population. In this
paper, we report size-segregated particle number concentrations observed at
a newly developed Beijing station during the winter of 2018. Our
measurements covered particle number size distributions over the diameter
range of 1.5 nm–1  µ m (cluster mode, nucleation mode, Aitken mode and
accumulation mode), thus being descriptive of a major fraction of the
processes taking place in the atmosphere of Beijing. Here we focus on
explaining the concentration variations in the observed particle modes, by
relating them to the potential aerosol sources and sinks, and on
understanding the connections between these modes. We considered haze days
and new particle formation event days separately. Our results show that
during the new particle formation (NPF) event days increases in cluster mode
particle number concentration were observed, whereas during the haze days
high concentrations of accumulation mode particles were present. There was a
tight connection between the cluster mode and nucleation mode on both NPF
event and haze days. In addition, we correlated the particle number
concentrations in different modes with concentrations of trace gases and
other parameters measured at our station. Our results show that the particle
number concentration in all the modes correlated with NOx , which
reflects the contribution of traffic to the whole submicron size range. We
also estimated the contribution of ion-induced nucleation in Beijing, and
we found this contribution to be negligible.

/pdf/variation-of-size-segregated-particle-number-concentrations-3rnqrm209z.pdf

Variation of size-segregated particle number concentrations in wintertime Beijing

This review discusses the developments in aerosol instrumentation that have led to the current vapor condensation based instruments capable of detecting sub-3 nm particles. We begin from selected r...

/pdf/review-of-sub-3-nm-condensation-particle-counters-js162vd7rh.pdf

Review of sub-3 nm condensation particle counters, calibrations, and cluster generation methods

Gaseous dicarboxylic acids (diacids) are suggested to participate in atmospheric new particle formation via bonding with sulfuric acid (SA), ammonia (NH3), amines, and other molecules. However, the...

Observational evidence for the involvement of dicarboxylic acids in particle nucleation

. Measuring particle size distribution accurately down to approximately 1 nm
is needed for studying atmospheric new particle formation. The scanning
particle size magnifier (PSM) using diethylene glycol as a working fluid has
been used for measuring sub-3 nm atmospheric aerosol. A proper inversion
method is required to recover the particle size distribution from PSM raw
data. Similarly to other aerosol spectrometers and classifiers, PSM inversion
can be deduced from a problem described by the Fredholm integral equation of
the first kind. We tested the performance of the stepwise method, the kernel
function method (Lehtipalo et al., 2014), the H&A linear inversion method
(Hagen and Alofs, 1983), and the expectation–maximization (EM) algorithm.
The stepwise method and the kernel function method were used in previous
studies on PSM. The H&A method and the expectation–maximization algorithm
were used in data inversion for the electrical mobility spectrometers and the
diffusion batteries, respectively (Maher and Laird, 1985). In addition, Monte
Carlo simulation and laboratory experiments were used to test the accuracy
and precision of the particle size distributions recovered using four
inversion methods. When all of the detected particles are larger than 3 nm,
the stepwise method may report false sub-3 nm particle concentrations
because an infinite resolution is assumed while the kernel function method
and the H&A method occasionally report false sub-3 nm particles because
of the unstable least squares method. The accuracy and precision of the
recovered particle size distribution using the EM algorithm are the best
among the tested four inversion methods. Compared to the kernel function
method, the H&A method reduces the uncertainty while keeping a similar
computational expense. The measuring uncertainties in the present scanning
mode may contribute to the uncertainties of the recovered particle size
distributions. We suggest using the EM algorithm to retrieve the particle
size distributions using the particle number concentrations recorded by the
PSM. Considering the relatively high computation expenses of the EM
algorithm, the H&A method is recommended for preliminary data analysis. We
also gave practical suggestions on PSM operation based on the inversion
analysis.

/pdf/data-inversion-methods-to-determine-sub-3-nm-aerosol-size-3xz4sq2dz5.pdf

Linlin Shi

Papers

Observational evidence for the involvement of dicarboxylic acids in particle nucleation

Data inversion methods to determine sub-3 nm aerosol size distributions using the particle size magnifier