Measurement report: Molecular characteristics of cloud water in southern China and insights into aqueous-phase processes from Fourier transform ion cyclotron resonance mass spectrometry

Characterizing the molecular composition of cloud water could provide unique insights into aqueous chemistry. Field measurements were conducted at Mt. Tianjing in southern China in May, 2018. There are thousands of formulas (C$_{5-30}$H$_{4-55}$O$_{1-15}$N$_{0-2}$S$_{0-2}$) identified in cloud water by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). CHON formulas (formulas containing C, H, O, and N elements; the same is true for CHO and CHOS) represent the dominant component (43.6 %–65.3 % of relative abundance), followed by CHO (13.8 %–52.1%). S-containing formulas constitute ∼5 %–20 % of all assigned formulas. Cloud water has a relative-abundance-weighted average O/C of 0.45–0.56, and the double bond equivalent of 5.10–5.70. Most of the formulas (>85 %) are assigned as aliphatic and olefinic species. No statistical difference in the oxidation state is observed between cloud water and interstitial PM$_{2.5}$. CHON with aromatic structures are abundant in cloud water, suggesting their enhanced in-cloud formation. Other organics in cloud water are mainly from biomass burning and oxidation of biogenic volatile organic compounds. The cloud water contains more abundant CHON and CHOS at night, which are primarily contributed by −N$_{2}$O$_{5}$ function and organosulfates, demonstrating the enhanced formation in dark aqueous or multi-phase reactions. While more abundant CHO is observed during the daytime, likely due to the photochemical oxidation and photolysis of N- or S-containing formulas. The results provide an improved understanding of the in-cloud aqueous-phase reactions

Enhanced daytime secondary aerosol formation driven by gas-particle partitioning in downwind urban plumes

Anthropogenic emissions from city clusters can significantly enhance secondary organic aerosol (SOA) formation in the downwind regions, while the mechanism is poorly understood. To investigate the effect of pollutants within urban plumes on organic aerosol (OA) evolution, a field campaign was conducted at a downwind site of the Pearl River Delta region of China in the fall of 2019. A time-of-flight chemical ionization mass spectrometer coupled with a Filter Inlet for Gases and Aerosol (FIGAERO-CIMS) was used to probe the gas- and particle-phase molecular composition and thermograms of organic compounds. For air masses influenced by urban pollution, strong daytime SOA formation through gas-particle partitioning was observed, resulting in higher OA volatility. The obvious SOA enhancement was mainly attributed to the equilibrium partitioning of non-condensable (C * ≥ 100.5 μg m-3) organic vapors. We speculated that the elevated NOx concentration could suppress the formation of highly oxidized products, resulting in a smooth increase of condensable (C * < 100.5 μg m-3) organic vapors. Evidence showed that urban pollutants (NOx and VOCs) could enhance the oxidizing capacity, while the elevated VOCs was mainly responsible for promoting daytime SOA formation by increasing the RO2 production rate. Our results highlight the important role of urban anthropogenic pollutants in SOA control in the suburban region

Impact of in-cloud aqueous processes on the chemical compositions and morphology of individual atmospheric aerosols

The composition, morphology, and mixing structure of individual cloud residues (RES) and interstitial particles (INT) at a mountaintop site were investigated. Eight types of particles were identified, including sulfate-rich (S-rich), S-organic matter (OM), aged soot, aged mineral dust, aged fly ash, aged metal, refractory, and aged refractory mixture. A shift of dominant particle types from S-rich (29 %) and aged soot (27 %) in the INT to aged refractory mixture (23 %) and S-OM (22 %) in the RES is observed. In particular, particles with organic shells are enriched in the RES (27 %) relative to the INT (12 %). Our results highlight that the formation of more oxidized organic matter in the cloud contributes to the existence of organic shells after cloud processing. The fractal dimension (D$_{f}$), a morphologic parameter to represent the branching degree of particles, for soot particles in the RES (1.82 ± 0.12) is lower than that in the INT (2.11 ± 0.09), which indicates that in-cloud processes may result in less compact soot. This research emphasizes the role of in-cloud processes in the chemistry and microphysical properties of individual particles. Given that organic coatings may determine the particle hygroscopicity, activation ability, and heterogeneous chemical reactivity, the increase of OM-shelled particles upon in-cloud processes should have considerable implications

Source and formation of fine particulate nitrate in South China: Constrained by isotopic modeling and online trace gas analysis

NOx is an important precursor of fine particulate matter (PM2.5) and O-3 and its oxidation product NO3- may be the main driver of PM2.5. In this study, 76 daily fine particle samples were collected from a site in South China, and the characteristics of NO3- were studied using an improved Bayesian mixing model based on delta N-15-delta O-18 compositions and online trace gas analysis. NO3- concentrations ranged from 0.60 to 21.7 mu g/m(3), accounting for 6.0 +/- 3.3% of total PM2.5 on average. delta N-15-NO3- values varied from -3.6 to 15.4 parts per thousand (average: 7.5 +/- 3.3 parts per thousand). Driven by changes in chemical pathways and environmental parameters, including equilibrium fractionation and thermodynamic effects, delta N-15-NO3- values were lowest in spring and highest in winter. delta O-18-NO3- values varied from 21.0 to 90.1 parts per thousand (average: 62.4 +/- 14.0 parts per thousand). In spring, the lowest delta O-18-NO(3)(-)values were observed due to high proportions originating from center dot HO2 and center dot OH reaction pathways, as well as changes in other parameters. In winter, the highest delta O-18-NO3- values were observed due to cold and dry weather, along with the lowest proportion from the center dot OH pathway. Moreover, the trace gases CO, O-3, NOx, SO2, and N2O5 were measured seasonally to determine the main drivers of seasonality in delta N-15-delta O-18 composition. Monte Carlo simulation was used to estimate the relative contributions of the gas-phase reactions of NO2 and center dot OH radicals and the hydrolysis of N2O5. The results showed that the center dot OH generation pathway was predominant throughout the year, with relative contributions of 72 +/- 18%, 76 +/- 16%, 63 +/- 17% and 39 +/- 15% in spring, summer, fall and winter, respectively. Coal combustion (50.1 +/- 13.8%) was the predominant source of NO3- identified using the Bayesian model, and originated from central and southern Guangdong Province, as indicated by potential source contribution function analysis

Source Apportionment of PM(2.5)in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon

To accurately apportion the sources of aerosols, a combined method of positive matrix factorization (PMF) and the Bayesian mixing model was applied in this study. The PMF model was conducted to identify the sources of PM(2.5)in Guangzhou. The secondary inorganic aerosol source was one of the seven main sources in Guangzhou. Based on stable isotopes of oxygen and nitrogen (delta N-15-NO(3)(-)and delta O-18-NO3-), the Bayesian mixing model was performed to apportion the source of NO(3)(-)to coal combustion, traffic emission and biogenic source. Then the secondary aerosol source was subdivided into three sources according to the discrepancy in source apportionment of NO(3)(-)between PMF and Bayesian mixing model results. After secondary aerosol assignment, the six main sources of PM(2.5)were traffic emission (30.6%), biomass burning (23.1%), coal combustion (17.7%), ship emission (14.0%), biomass boiler (9.9%) and industrial emission (4.7%). To assess the source apportionment results, fossil/non-fossil source contributions to organic carbon (OC) and element carbon (EC) inferred from(14)C measurements were compared with the corresponding results in the PMF model. The results showed that source distributions of EC matched well between those two methods, indicating that the PMF model captured the primary sources well. Probably because of the lack of organic molecular markers to identify the biogenic source of OC, the non-fossil source contribution to OC in PMF results was obviously lower than(14)C results. Thus, an indicative organic molecular tracer should be used to identify the biogenic source when accurately apportioning the sources of aerosols, especially in the region with high plant coverage or intense biomass burning

Reconstructed Light Extinction Coefficients of Fine Particulate Matter in Rural Guangzhou, Southern China

A one-year campaign was conducted to collected PM2.5 samples in the rural area of Guangzhou, the largest megacity in South China, from March 2012 to February 2013. Mass concentration of PM2.5, carbonaceous fractions (i.e., organic carbon (OC) and elemental carbon (EC)) and 6 water-soluble ions were analyzed. Light extinction coefficient (b(ext)) of fine particulate matter was reconstructed using the revised IMPROVE formula at the site. The reconstructed b(ext) was compared with the measured b(ext) converted from visibility. A good correlation was obtained between the two sets of b(ext) with a coefficient of determination (R-2) of 0.61 and a slope of 0.99. The average reconstructed b(ext) in the study was 253.7 +/- 162.9 Mm(-1). The seasonal reconstructed b(ext) was in the order of autumn (319.4 +/- 207.2 Mm(-1)) > winter (269.6 +/- 175.5 Mm(-1)) > summer (219.0 +/- 129.3 Mm(-1)) > spring (193.3 +/- 94.9 Mm(-1)). (NH4)(2)SO4 (AS) made a dominant contribution to the light extinction budget, accounting for 61.3% (155.6 +/- 108.5 Mm(-1)) annually, with highest in autumn (68.0%) and lowest in winter (55.2%). Organic matter (OM) was the second largest contributor accounting for 20.5% (52.2 +/- 42.7 Mm(-1)) with highest in winter (23.4%) and lowest in spring (18.0%). The relationship between reconstructed b(ext) and measured bext was investigated under the influence of seasonality, visibility and PM2.5 concentration. We found that b(ext) could be reconstructed using revised IMPROVE formula in high PM2.5 days (threshold value of similar to 60 mu g m(-3)). On other hand, the performance of formula was unsatisfactory for b(ext) reconstruction of in low PM2.5 days, when meteorological conditions could have significant impact on visibility

Volatile Organic Compounds Monitored Online at Three Photochemical Assessment Monitoring Stations in the Pearl River Delta (PRD) Region during Summer 2016: Sources and Emission Areas

Volatile organic compounds (VOCs) were monitored online at three photochemical assessment monitoring stations (MDS, WQS and HGS) in the Pearl River Delta region during the summer of 2016. Measured levels of VOCs at the MDS, WQS and HGS sites were 34.78, 8.54 and 8.47 ppbv, respectively, with aromatics and alkenes as major ozone precursors and aromatics as major precursors to secondary organic aerosol (SOA). The positive matrix factorization (PMF) model revealed that VOCs at the sites mainly came from vehicle exhaust, petrochemical industry, and solvent use. Vehicle exhaust and industrial processes losses contributed most to ozone formation potentials (OFP) of VOCs, while industrial processes losses contributed most to SOA formation potentials of VOCs. Potential source contribution function (PSCF) analysis revealed a north-south distribution for source regions of aromatics occurring at MDS with emission sources in Guangzhou mainly centered in the Guangzhou central districts, and source regions of aromatics at WQS showed an east-west distribution across Huizhou, Dongguan and east of Guangzhou, while that at HGS showed a south-north distribution across Guangzhou, Foshan, Zhaoqing and Yangjiang. This study demonstrates that multi-point high time resolution data can help resolve emission sources and locate emission areas of important ozone and SOA precursors