Molecular characteristics of ambient organic aerosols in Shanghai winter before and after the COVID-19 outbreak.

During the global pandemic of COVID-19, the world adopted different strategies to avoid the human and economic loss, and so does China. The reduction of human activities during this time period caused reduction in PM emissions. This study adopted a HPLC-Q-TOF-MS to compare the chemical compositions of ambient aerosol samples collected in Shanghai winter before (2018, 2019) and after (2021) the COVID-19 outbreak. The identified compositions were classified into subgroups of CHO, CHN, CHON, CHONS, CHOS and CHN compounds. Results showed that CHO compounds and CHON compounds were dominating the organic compounds in ESI- and ESI+, respectively. The average percentages of CHO- compounds were 57.97 % in 2018, 58.98 % in 2019, and 43.93 % in 2021, respectively. The average percentages of CHON+ compounds were 52.74 % in 2018, 50.34 % in 2019, and 52.02 % in 2021, respectively. The proportion of aliphatic compounds increased gradually during the three years, especially in 2021, indicating that CHO compounds were less affected by aromatic precursors after the COVID-19 outbreak. The contribution of anthropogenic emissions in Shanghai was weakened compared with the previous years. In addition, there was an enhanced emission source containing hydroxyl for CHOS compound formation in 2021. The variations of atmospheric oxidation degree among the three years were not significant.

Molecular characteristics, sources and environmental risk of aromatic compounds in particulate matter during COVID-2019: Nontarget screening by ultra-high resolution mass spectrometry and comprehensive two-dimensional gas chromatography.

Aromatic compounds, including many polycyclic aromatic hydrocarbons (PAHs), are suspected carcinogens and may originate from different sources. To investigate the impact of anthropogenic emission reductions on unknown aromatic compounds in particulate matter, we collected samples during the pre-COVID period in 2020, the COVID-19 lockdown period in 2020, and the same period as the lockdown in 2019. Besides the 16 PAHs, other aromatic compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. Four main compound classes were identified: CH, CHO, CHNO, and CHOS. Hierarchical cluster analysis showed the aromatic compounds varied during the different periods. Compared with before the pandemic, the relative abundances of aromatic compounds with low degrees of unsaturation and long alkyl chains (e.g., alkylbenzenes) increased. These compounds probably mainly arose from fossil fuel combustion and petrochemical industry emissions. The CHO compounds, which were dominated by those with high degrees of oxidation, might originate from secondary organic aerosols. Aromatic aldehydes (e.g., cyclamen aldehyde) and benzoates (e.g., 2-ethylhexyl benzoate) probably with high toxicity deserve more attention. During lockdown, nitro derivatives of condensed PAHs were the main CHNO compounds, and the numbers of homologs decreased perhaps because of significant reductions in NOx and PAHs. CHOS compounds with long carbon chains and low degrees of unsaturation were predominant and the numbers of homologs increased. Five compounds (e.g. 1,3-dimethyl pyrene) were predicted to possibly exhibit persistent and bio-accumulated by EPI Suite model, which need further research. The results provide insight on aromatic compounds and their source appointment in atmospheric particulate matter.

Recent Developments in the Determination of PM2.5 Chemical Composition.

Fine particulate matter (named PM2.5) has become a prominent and dangerous form of air pollution. The chemical composition of PM2.5 mainly includes inorganic elements, water soluble ions, elemental carbon (EC), organic carbon (OC), and organic compounds. The detection method for inorganic elements mainly includes X ray fluorescence, inductively coupled plasma-atomic emission spectrometry, and inductively coupled plasma mass spectrometry. As for water soluble ions, ion chromatography is the most common detection method. EC and OC are usually detected by carbon analyzer. The organic compounds are determined by gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry. In this paper, the merits and drawbacks of each analytical methods for the determination of PM2.5 chemical composition are summarized. This review also includes our discussion on the improvement of the analytical accuracy for the determination of PM2.5 chemical composition owing to the development of reference materials.

Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools.

Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis two-dimensional gas chromatography (GCxGC)-mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC-MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects.

Solid-Phase Microextraction Fiber in Face Mask for in Vivo Sampling and Direct Mass Spectrometry Analysis of Exhaled Breath Aerosol.

Molecular analysis of exhaled breath aerosol (EBA) with simple procedures represents a key step in clinical and point-of-care applications. Due to the crucial health role, a face mask now is a safety device that helps protect the wearer from breathing in hazardous particles such as bacteria and viruses in the air; thus exhaled breath is also blocked to congregate in the small space inside of the face mask. Therefore, direct sampling and analysis of trace constituents in EBA using a face mask can rapidly provide useful insights into human physiologic and pathological information. Herein, we introduce a simple approach to collect and analyze human EBA by combining a face mask with solid-phase microextraction (SPME) fiber. SPME fiber was inserted into a face mask to form SPME-in-mask that covered nose and mouth for in vivo sampling of EBA, and SPME fiber was then coupled with direct analysis in real-time mass spectrometry (DART-MS) to directly analyze the molecular compositions of EBA under ambient conditions. The applicability of SPME-in-mask was demonstrated by direct analysis of drugs and metabolites in oral and nasal EBA. The unique features of SPME-in-mask were also discussed. Our results showed that this method is enabled to analyze volatile and nonvolatile analytes in EBA and is expected to have a significant impact on human EBA analysis in clinical applications. We also hope this method will inspire biomarker screening of some respiratory diseases that usually required wearing of a face mask in daily life.