Abstract. Particulate matter (PM) pollution in China is an emerging
environmental issue which policy makers and the public have increasingly paid
attention to. In order to investigate the characteristics, sources, and
chemical processes of PM pollution in Guangzhou, field measurements were
conducted from 20 November 2017 to 5 January 2018, with a time-of-flight
aerosol chemical speciation monitor (ToF-ACSM) and other collocated
instruments. Mass concentrations of non-refractory submicron particulate
matter (NR-PM1) measured by the ToF-ACSM correlated well with
those of PM2.5 or PM1.1 measured by filter-based methods. The
organic mass fraction increased from 45 % to 53 % when the air switched
from non-pollution periods to pollution episodes (EPs), indicating significant
roles of organic aerosols (OAs) during the whole study. Based on the mass
spectra measured by the ToF-ACSM, positive matrix factorization (PMF) with
the multilinear engine (ME-2) algorithm was performed to deconvolve OA into four
factors, including hydrocarbon-like OA (HOA, 12 %), cooking OA (COA,
18 %), semi-volatile oxygenated OA (SVOOA, 30 %), and low-volatility
oxygenated OA (LVOOA, 40 %). Furthermore, we found that SVOOA and nitrate
were significantly contributed from local traffic emissions while sulfate
and LVOOA were mostly attributed to regional pollutants. Comparisons between
this work and other previous studies in China show that secondary organic aerosol (SOA) fraction in
total OA increases spatially across China from the north to the south. Two distinctly opposite trends for NR-PM1 formation were observed
during non-pollution periods and pollution EPs. The ratio of secondary PM
(SPM = SVOOA + LVOOA + sulfate + nitrate + ammonium) to primary PM
(PPM = HOA + COA + chloride), together with peroxy radicals RO2∗
and ozone, increased with increasing NR-PM1 concentration during
non-pollution periods, while an opposite trend of these three quantities was
observed during pollution EPs. Furthermore, oxidation degrees of both OA and
SOA were investigated using the f44∕f43 space and the results show
that at least two OOA factors are needed to cover a large range of
f44 and f43 in Guangzhou. Comparisons between our results and other
laboratory studies imply that volatile organic compounds (VOCs) from traffic
emissions, in particular from diesel combustion and aromatic compounds, are the
most likely SOA precursors in Guangzhou. Peroxy radical RO2∗ was used as a tracer for SOA formed through gas-phase oxidation. For
non-pollution periods, SOA concentration was reasonably correlated with
RO2∗ concentration during both daytime and nighttime, suggesting that
gas-phase oxidation was primarily responsible for SOA formation. However,
there was no correlation between SOA and RO2∗ in pollution EPs,
suggesting a dramatically changed mechanism for SOA formation. This conclusion
can also be supported by different features of SOA in a van Krevelen diagram
between non-pollution periods and pollution EPs. Furthermore, for pollution
EPs, when NR-PM1 mass concentration was divided into six segments, in
each segment except for the lowest one SOA concentration was correlated
moderately with RO2∗ concentration, suggesting that gas-phase oxidation
still plays important roles in SOA formation. The intercepts of the above
linear regressions, which likely correspond to the extent of other
mechanisms (i.e., heterogeneous and multiphase reactions), increase with
increasing NR-PM1 mass concentration. Our results suggest that while
gas-phase oxidation contributes predominantly to SOA formation during
non-pollution periods, other mechanisms such as heterogeneous and multiphase
reactions play more important roles in SOA formation during pollution EPs
than gas-phase oxidation.