Nontarget Screening Exhibits a Seasonal Cycle of PM2.5 Organic Aerosol Composition in Beijing

Regional differences in molecular characteristics of atmospheric water-soluble organic carbon over northern China: Comparison of remote, rural, and urban environments

Atmospheric water-soluble organic carbon (WSOC) is a critical component of airborne particulates. It significantly affects the Earth's energy balance, air quality, and human health. Despite its importance, the molecular composition and sources of WSOC remain unclear, particularly in non-urban areas. In this study, we collected total suspended particulate (TSP) samples from three sites in northern China: Erenhot (remote site), Zhangbei (rural site), and Jinan (urban site). The WSOC components were analyzed using high-performance liquid chromatography coupled with high-resolution mass spectrometry. The results showed that the formula numbers of identified compounds exhibited a decreasing trend of Jinan (2647) > Zhangbei (2046) > Erenhot (1399). Among the assigned formulas, CHO compounds were the most abundant category for all three sites, accounting for 33 %-38 % of the identified compounds, followed by the CHON compounds with contributions of 27 %-30 %. In the remote site of Erenhot, CHO compounds were dominated by oxidized unsaturated organic compounds, and CHON compounds were mainly low-oxygenated aliphatic compounds, suggesting a significant influence of primary emissions. In contrast, the urban site of Jinan showed higher contributions of CHO and CHON compounds with elevated oxidation degrees, indicating the influence of more extensive secondary oxidation processes. Atmospheric WSOC in Erenhot and Zhangbei had abundant reduced sulfur-containing species, likely from coal or diesel combustion, while that in Jinan was characterized by aliphatic organosulfates and nitrooxy-organosulfates, which are mainly associated with traffic emissions and biogenetic sources, respectively. These findings reveal significant differences in the molecular composition of WSOC in different atmospheric environments and improve our understanding of the chemical properties, potential sources, and transformations of organic aerosols.

Recognition of screening out hierarchical toxic contaminants tuned by quantified pseudo-components from complex engineering co-combustion

Co-combustion of industrial and municipal solid wastes has emerged as the most promising disposal technology, yet its effect on unknown contaminants generation remains rarely revealed due to waste complexity. Hence, six batches of large-scale engineering experiments were designed in an incinerator of 650 t/d, which overcame the inauthenticity and deviation of laboratory tests. 953-1772 non-targeted compounds were screened in fly ash. Targeting the impact of co-combustion, a pseudo-component matrix model was innovatively integrated to quantitatively extract nine components from complex wastes grouped into biomass and plastic. Thus, the influence was evaluated across eight dimensions, covering molecular characteristics and toxicity. The effect of co-combustion with biomass pseudo-components was insignificant. However, co-combustion with high ratios of plastic pseudo-components induced higher potential risks, significantly promoting the formation of unsaturated hydrocarbons, highly unsaturated compounds (DBE≥15), and cyclic compounds by 19 %- 49 %, 17 %- 31 %, and 7 %- 27 %, respectively. Especially, blending with high ratios of PET plastic pseudo-components produced more species of contaminants. Unique 2 Level I toxicants, bromomethyl benzene and benzofuran-2-carbaldehyde, as well as 4 Level II toxicants, were locked, receiving no concern in previous combustion. The results highlighted risks during high proportion plastics co-combustion, which can help pollution reduction by tuning source wastes to enable healthy co-combustion.

Multilevel Screening Strategy to Identify the Hydrophobic Organic Components of Ambient PM2.5 Associated with Hepatocellular Steatosis

Hepatic steatosis is the first step in a series of events that drives hepatic disease and has been considerably associated with exposure to fine particulate matter (PM2.5). Although the chemical constituents of particles matter in the negative health effects, the specific components of PM2.5 that trigger hepatic steatosis remain unclear. New strategies prioritizing the identification of the key components with the highest potential to cause adverse effects among the numerous components of PM2.5 are needed. Herein, we established a high-resolution mass spectrometry (MS) data set comprising the hydrophobic organic components corresponding to 67 PM2.5 samples in total from Taiyuan and Guangzhou, two representative cities in North and South China, respectively. The lipid accumulation bioeffect profiles of the above samples were also obtained. Considerable hepatocyte lipid accumulation was observed in most PM2.5 extracts. Subsequently, 40 of 695 components were initially screened through machine learning-assisted data filtering based on an integrated bioassay with MS data. Next, nine compounds were further selected as candidates contributing to hepatocellular steatosis based on absorption, distribution, metabolism, and excretion evaluation and molecular dockingin silico. Finally, seven components were confirmed in vitro. This study provided a multilevel screening strategy for key active components in PM2.5 and provided insight into the hydrophobic PM2.5 components that induce hepatocellular steatosis.

PM2.5 exposure contributes to anxiety and depression-like behaviors via phenyl-containing compounds interfering with dopamine receptor

As a global problem, fine particulate matter (PM2.5) really needs local fixes. Considering the increasing epidemiological relevance to anxiety and depression but inconsistent toxicological results, the most important question is to clarify whether and how PM2.5 causally contributes to these mental disorders and which components are the most dangerous for crucial mitigation in a particular place. In the present study, we chronically subjected male mice to a real-world PM2.5 exposure system throughout the winter heating period in a coal combustion area and revealed that PM2.5 caused anxiety and depression-like behaviors in adults such as restricted activity, diminished exploratory interest, enhanced repetitive stereotypy, and elevated acquired immobility, through behavioral tests including open field, elevated plus maze, marble-burying, and forced swimming tests. Importantly, we found that dopamine signaling was perturbed using mRNA transcriptional profile and bioinformatics analysis, with Drd1 as a potential target. Subsequently, we developed the Drd1 expression-directed multifraction isolating and nontarget identifying framework and identified a total of 209 compounds in PM2.5 organic extracts capable of reducing Drd1 expression. Furthermore, by applying hierarchical characteristic fragment analysis and molecular docking and dynamics simulation, we clarified that phenyl-containing compounds competitively bound to DRD1 and interfered with dopamine signaling, thereby contributing to mental disorders. Taken together, this work provides experimental evidence for researchers and clinicians to identify hazardous factors in PM2.5 and prevent adverse health outcomes and for local governments and municipalities to control source emissions for diminishing specific disease burdens.

Molecular insights into microbial transformation of bioaerosol-derived dissolved organic matter discharged from wastewater treatment plant

Wastewater treatment plants (WWTP) are important sources of aerosol-derived dissolved organic matter (ADOM) which may threaten human health via the respiratory system. In this study, aerosols were sampled from a typical WWTP to explore the chemical molecular diversity, molecular ecological network, and potential toxicities of the ADOM in the aerosols. The high fluorescence index (>1.9) and biological index (0.66-1.17) indicated the strong autogenous microbial source characteristics of the ADOM in the WWTP. DOM and microbes in the wastewater were aerosolized due to strong agitation and bubbling in the treatment processes, and contributed to 74 % and 75 %, respectively, of the ADOM and microbes in the aerosols. The ADOM was mainly composed of CHO and CHOS accounting for 35 % and 29 % of the total number of molecules, respectively, with lignin-like (69 %) as the major constituent. 49 % of the ADOM transformations were thermodynamically limited, and intragroup transformations were easier than intergroup transformations. Bacteria in the aerosols involved in ADOM transformations exhibited both cooperative and divergent behaviors and tended to transform carbohydrate-like and amino sugar/protein-like into recalcitrant lignin-like. The microbial compositions were affected by atmosphere temperature and humidity indirectly by modulating the properties of ADOM. Tannin-like, lignin-like, and unsaturated hydrocarbon-like molecules in the ADOM were primary toxicity contributors, facilitating the expression of inflammatory factors IL-β (2.2-5.4 folds), TNF-α (3.5-7.0 folds), and IL-6 (3.5-11.2 folds), respectively.

Comparative analysis of organic chemical compositions in airborne particulate matter from Ulaanbaatar, Beijing, and Seoul using UPLC-FT-ICR-MS and artificial neural network

This paper presents comparative study on the composition and sources of PM2.5 in Ulaanbaatar, Beijing, and Seoul. Ultrahigh performance liquid chromatography (UPLC) combined with ultrahigh resolution mass spectrometry (UHR-MS) were employed to analyze 85 samples collected in winter. The obtained 340 spectra were interpreted with artificial neural network (ANN). PM2.5 mass concentrations in Ulaanbaatar were significantly higher than those in Beijing and Seoul. ANN based interpretation of UPLC UHR-MS data showed that aliphatic/lipid derived organo‑sulfur compounds, polycyclic aromatic and organo‑oxygen compounds were characteristic to Ulaanbaatar. Whereas, aliphatic/lipid-derived organo‑oxygen compounds were major components in Beijing and Seoul. Aromatic organo‑nitrogen compounds were the main contributors to differentiating the spectra obtained from Beijing from the other cities. Based on two-dimensional gas chromatography/high resolution mass spectrometric (GCxGC/HRMS) data, it was determined that the concentrations of the polycyclic aromatic hydrocarbon (PAH) and polycyclic aromatic sulfur heterocycle (PASH) containing sulfur were highest in Ulaanbaatar, followed by Beijing and Seoul. Coal/biomass combustion was identified as the primary source of contamination in Ulaanbaatar, while petroleum combustion was the main contributor to PM2.5 in Beijing and Seoul. The conclusion that diesel-powered heavy-duty trucks and buses are the main contributors to NOx emissions in Beijing is consistent with previous reports. This study provides a more comprehensive understanding of the composition and sources of PM2.5 in the three cities, with a focus on the differences in their atmospheric pollution profiles based on the UPLC UHR-MS and ANN analysis. It is notable that this study is the first to utilize this method on a large-scale sample set, providing a more detailed and molecular-level understanding of the compositional differences among PM2.5. Overall, the study contributes to a better understanding of the sources and composition of PM2.5 in Northeast Asia, which is essential for developing effective strategies to reduce air pollution and improve public health.

Particulate matter pollution and non-targeted analysis of polar compounds in three regions of Brazil

Atmospheric Particulate Matter (PM) is a pollutant with diverse origins, exhibiting varying chemical compositions, and undergoes several molecular transformations in the atmosphere. In this study, PM samples (PM2.5, PM10 and TSP) were collected in five Brazilian cities (Camboriú-SC; Catalão-GO; Florianópolis-SC; Limeira-SP and Novo Hamburgo-RS) during the four seasons of the year. Analysis of Variance (ANOVA) was used to evaluate the differences between each city and season in PM concentration. PM10 average concentrations were higher in the city of Limeira, compared to the other (ANOVA p-values and Tukey's test). Moreover, Tukey's test demonstrated differences between the average PM10 concentrations in summer and winter. Regarding TSP and PM2.5, Tukey's test showed differences between winter and warm seasons (spring and summer). Moreover, polar compounds from the samples collected in the summer (February) and winter (August) periods were analyzed (Ultra-High-Performance Liquid Chromatography coupled to a Quadrupole Time-of-Flight Mass Spectrometer) following a non-targeted approach and annotated. This is the first study to carry out this type of analysis in these five Brazilian cities. Despite the differences in PM concentrations, profiles of polar organic compounds, showed similarities between samples/and, in general, the same compounds were present, albeit with different intensities. The annotated compounds are associated with vehicle emissions and plastics, which are considered important global air polluters. Therefore, there is an urgent necessity for comprehensive studies aimed at investigating the non-targeted compounds existing in the atmosphere. Such research can provide invaluable insights to policymakers, enabling them to formulate effective guidelines and policies to mitigate particulate matter concentration and enhance overall air quality.