Characterization of submicron particles by time-of-flight aerosol chemical speciation monitor (ToF-ACSM) during wintertime: aerosol composition, sources, and chemical processes in Guangzhou, China

Abstract

<jats:p>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. </jats:p>

Roles of Semivolatile/Intermediate‐Volatility Organic Compounds on SOA Formation Over China During a Pollution Episode: Sensitivity Analysis and Implications for Future Studies

“…Furthermore, the highest monthly mean PM 2.5 concentrations during winter 2017 in Fenwei Plain, Sichuan Basin, Beijing‐Tianjin‐Hebei, Yangtze River Delta, and Pearl River Delta regions were 143 μg/m 3 , 88 μg/m 3 , 75 μg/m 3 , 71 μg/m 3 , and 55 μg/m 3 , respectively (Zhai et al., 2019). In addition, the PM 1 concentrations during winter for China are typically dominated by OA (Gu et al., 2011; Guo et al., 2020). Last but not least, simulation of winter pollution events could greatly improve the understanding of impacts of S/IVOCs on SOA formation and its mechanism under extreme polluted conditions.…”

Real‐Time Characterization of Aerosol Compositions, Sources, and Aging Processes in Guangzhou During PRIDE‐GBA 2018 Campaign

“…J. Guo et al. (2020) investigated the sources of OA with an Aerosol Chemical Speciation Monitor (ACSM, simplified version of AMS), and suggested SOA formation during the polluted periods in Guangzhou was mainly governed by heterogeneous/multiphase reactions, while needs more accurate and direct evidences.…”

Measurement report: Emissions of intermediate-volatility organic compounds from vehicles under real-world driving conditions in an urban tunnel

“…−1 (average ±95 % CI, confidence interval) (Fig.1). The average EF IVOCs for DVs and GVs could be further derived through linear regression as below(Ho et al, 2007;Kramer et al, 2020):…”