Gaseous sulfuric acid (SA) has received a lot of
attention for its crucial role in atmospheric new particle formation (NPF). And for this reason, studies until now have mainly focused on daytime SA
when most NPF events occur. While daytime SA production is driven by
SO
Atmospheric aerosol particles have considerable impact on global climate by
directly affecting the radiation balance of the earth and by indirectly
acting as cloud condensation nuclei (Stocker et al., 2014). The number
concentration of these aerosol particles depends to a large extent on the
atmospheric new particle formation (NPF), which includes gas-phase
nucleation and subsequent growth of newly formed particles. Studies over the
past 20 years have shown that SA is the major gaseous precursor of NPF
in most environments inside the continental boundary layer
(Lee et al., 2019). SA-driven NPF can proceed as SA-H
Due to the crucial role of SA in NPF, accurate and reliable measurement of
SA is of great importance. Up to now, ambient SA concentrations have been
reported for many sites (Weber et al., 1997, 1998, 1999; Paasonen et al., 2010; Jokinen et al., 2018; Fiedler et al.,
2005; Eisele et al., 2006; Boy et al., 2008; Iida et al., 2008; Wang et al.,
2011; Kürten et al., 2016; Yao et al., 2018; Mauldin et al., 2001; Erupe et
al., 2010; Yu et al., 2014). These studies indicate that the concentration
level of SA in the atmosphere is closely related to human activities. In
general, daytime SA concentration is around 10
Due to the strong connection between SA and NPF, previous studies mostly focused on understanding the SA formation in the daytime. However, recent observation on the formation of sub-3 nm particles have shown that these cluster mode particles also exist with high concentration during the night (Junninen et al., 2008; Lehtipalo et al., 2011; Kulmala et al., 2013; Kecorius et al., 2015; Mazon et al., 2016; Yu et al., 2014) and sometimes even nighttime particle nucleation events can be clearly distinguished. In boreal forest environments, nighttime cluster formation can be attributed to highly oxygenated organic molecules (HOMs) (Kammer et al., 2018; Rose et al., 2018). However, the sources of SA and its role in the particle formation during nighttime remain largely unresolved, and both are the focus of this work. In this study, we show frequent and noticeable increase in SA during nighttime in urban Beijing. We further investigate the main sources of SA and demonstrate its role in the nocturnal formation of sub-3 nm clusters.
During the daytime, gaseous SA is primarily a photochemical product
generated from the oxidation of SO
The continuous and comprehensive measurements were conducted at the west
campus of Beijing University of Chemical Technology (39.95
Sulfuric acid was measured by a long time-of-flight chemical ionization mass
specter (LTOF-CIMS, Aerodyne Research, Inc.) equipped with a nitrate
chemical ionization source. The basic working principle of this instrument
can be found elsewhere (Jokinen et al., 2012). In our
measurement, we draw air through a stainless-steel tube with a length of 1.6 m and a diameter of
The quantification of sulfuric acid is derived from the ratio of bisulfate
ions (with counting rates unit in ions s
The calibration coefficient,
Six alkenes are analyzed in this study, i.e., propylene, butylene, butadiene, isoprene, pentene and hexene, which were detected by a single photon ionization time-of-flight mass spectrometer (SPI-MS 3000, Guangzhou Hexin Instrument Co., Ltd., China) (Gao et al., 2013). It should be mentioned that this instrument cannot distinguish isomers, and therefore the pentene and haxene could also be cyclopentane and cyclohexane, respectively. A polydimethylsiloxane (PDMS) membrane sampling system is used. As the PDMS membrane has better selective adsorption to volatile organic compounds (VOCs), VOC molecules can be concentrated after diffusing and desorbing from the membrane under vacuum sampling conditions. In this way, the detection limit of VOCs can be improved. Then the gas molecules are guided to an ionization chamber through a 2 mm diameter stainless steel capillary, where VOC molecules with an ionization energy smaller than 10.8 eV are ionized by the vacuum ultraviolet (VUV) light. For the detection of positive ions, two microchannel plates (MCPs; Hamamatsu, Japan) assembled with a chevron-type configuration are employed. An analog-to-digital-converter (ADC) was used to measure and record the output current signal from the MCPs.
Alkene concentrations are quantified by performing a direct calibration. The PAMS (Photochemical Assessment Monitoring Stations) and TO-15 environmental gases (including 57 and 65 types of VOCs separately; Linde Gas North America LLC, USA) are used as two standard gases with ultra-high-purity nitrogen as the carrier gas. Gases with different concentrations of VOCs are produced by mixing a constant carrier gas with standard gas of varying flow rates. The calibration coefficient is further calculated from the ratio between the actual concentration and the ion intensity.
The number concentration of clusters with the size range of 1.30–2.45 nm was measured with a particle sizer magnifier
(PSM) (Vanhanen et al., 2011), and the integrated number
concentration of particles from PSM is referred to as
An overview of our measurements during 18 January to 15 March 2019 is shown in Fig. 1. As our measurement period overlaps with the heating
period in Beijing (from 15 November 2018 to 15 March 2019),
the SO
Overview of different parameters measured from 18 January to 15 March 2019 for
In this work, the nighttime window is defined between 20:00 and 04:00 (the next day) to exclude any possible influence of photochemistry. Figure 2 shows the
diurnal variation of SA concentration on one typical SA event night
(14 March 2019) and one typical SA non-event night (3 February 2019). In general, nighttime SA concentration varies between 3.0
Daily variation of SA concentration on a typical night with a nighttime SA event (red line, 14 March 2019) and on a non-event night (blue line, 3 February 2019). The shaded blue area shows the period that is considered as nighttime in this study.
We further analyzed the features of nighttime SA event nights based on the
abovementioned 18 event nights and 16 non-event nights (Fig. 3). On SA
event nights, the mass concentration of PM
Boxplots for the concentrations of SO
We further investigated the determining factor for the occurrence of SA
events by looking into different variables during the SA event nights. CS
measurements were available for 13 event nights, during which 15 SA peaks
were observed. In general, we found that eight events (53 %) were mainly
associated with the decrease in CS. This is demonstrated in Fig. 4, where
the nighttime events as well as the simultaneous decrease in CS are
highlighted with green dots. Four other cases (27 %) were mostly due to
the increase in SO
Daily time series of different variables on nighttime SA event days
when SA events occurred under CS decrease conditions. Panel
As discussed above, nighttime SA events mainly occurred under clean conditions with a low CS value. Therefore, we classified all the nighttime data into three groups according to the air pollution level, which is assessed by visibility. The division standards for pollution level is explained in detail in Sect. S3. The clean (named Clean-1), mildly polluted and heavily polluted conditions are defined by visibility values which are larger than 12.0 km, in the range of 4.0–12.0 km and smaller than 4.0 km, respectively. Accordingly, data points under each condition took up 48 %, 25 % and 27 % of all data points.
After classifying the data set into groups based on the pollution level, the
balance between SA source and sink for each group was investigated
separately. The ozonolysis of alkenes under dark conditions is capable of
generating SCIs as well as OH radicals, both of which are able to oxidize
SO
Figure 5 shows the nighttime correlation between SA source terms and sink terms under different pollution levels, with the data binned by SA sources. If the
source and the sink terms correlate, then the slope represents the overall
apparent rate constant (
Under Clean-1 conditions (Fig. 5a), the source term and sink term have a
good linear correlation (
Correlation between the source term ([SO
If we compare the SA source and sink correlation between the Clean-1 (Fig. 5a) and Clean-2 (Fig. 5b) conditions, it is obvious that the slope of the
linear region of Clean-1 condition data points (2.7
Under mildly polluted conditions (Fig. 5c), the source and sink term also
have a good linear correlation when the source value exceeds 2.5
A well-tuned box model is a useful tool to resolve it and verify the role of
the ozonolysis of alkenes on the nighttime SA formation. However, such a
modeling work is not included in our study, as the lack of a complete VOC
data sets and the large uncertainties in yields of SCIs and oxidation rate
constants of SO
We show that the ozonolysis of alkenes is the major source for the
considerable amount of SA that exists at night, at least under unpolluted
conditions. As well as this, we found that increasing SA concentration coincided
with increasing number concentration of sub-3 nm particles (Fig. 6a),
suggesting that SA had a strong enhancement in the formation of newly formed
particles, which is consistent with a previous study (Cai et al., 2017).
Different from SA, there was a negative correlation between the
concentration of highly oxygenated organic molecules (HOMs) and
Continuous SA measurement was conducted during the heating-supply period in
urban Beijing. Frequent nighttime SA events were found and accounted for
about 32 % of the total measurement nights. Most nighttime SA events were
observed under unpolluted conditions and associated with a distinct drop in
CS. We show that the SA source corresponding to the product of O
All the data for plotting figures in the main text as well as the Supplement are
available and can be downloaded from
The supplement related to this article is available online at:
CY, MK, LY, YL, LD and FB are acknowledged for valuable discussions. CL, ZF, YZ, ZL, RY and YW provided the relevant measurement data. And DS, RC, KRD, JeK, JuK and BC contributed to the article modification.
The authors declare that they have no conflict of interest.
Heikki Junninen is acknowledged for providing the tofTool package used for processing LTOF-CIMS data. Kaspar R. Daellenbach acknowledges the support from the SNF mobility grant P2EZP2_181599.
This project has received funding from the National Natural Science Foundation of China (41877306), the ERC advanced grant no. 742206, the European Union's Horizon 2020 research and innovation program under grant agreement no. 654109, the Academy of Finland Center of Excellence Project no. 27204. Open-access funding was provided by the Helsinki University Library.
This paper was edited by Sally E. Pusede and reviewed by three anonymous referees.