Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning.

With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.

Characteristics of Volatile Organic Compounds Emitted from Airport Sources and Their Effects on Ozone Production.

In recent years, commercial air transport has increased considerably. However, the compositions and source profiles of volatile organic compounds (VOCs) emitted from aircraft are still not clear. In this study, the characteristics of VOCs (including oxygenated VOCs (OVOCs)) emitted from airport sources were measured at Shenzhen Bao'an International Airport. The results showed that the compositions and proportions of VOC species showed significant differences as the aircraft operating state changed. OVOCs were the dominant species and accounted for 63.17%, 58.44%, and 51.60% of the total VOC mass concentration during the taxiing, approach, and take-off stages. Propionaldehyde and acetone were the main OVOCs, and dichloromethane and 1,2-dichloroethane were the main halohydrocarbons. Propane had the highest proportion among all alkanes, while toluene and benzene were the predominant aromatic hydrocarbons. Compared with the source profiles of VOCs from construction machinery, the proportions of halogenated hydrocarbons and alkanes emitted from aircraft were significantly higher, as were those of propionaldehyde and acetone. OVOCs were still the dominant VOC species in aircraft emissions, and their calculated ozone formation potential (OFP) was much higher than that of other VOC species at all stages of aircraft operations. Acetone, propionaldehyde, formaldehyde, acetaldehyde, and ethylene were the greatest contributors to ozone production. This study comprehensively measured the distribution characteristics of VOCs, and its results will aid in the construction of a source profile inventory of VOCs emitted from aircraft sources in real atmospheric environments.

Influence of ozone pollution on the mixing state and formation of oxygenated organics containing single particles.

The formation and aging processes of oxygenated organic molecules (OOMs) are important for understanding the formation mechanisms of secondary organic aerosols (SOAs) in the field. In this study, we investigated the mixing states of OOM particles by identifying several oxygenated species along with the distributions of secondary organic carbon (SOC) during both clean and ozone (O3)-polluted periods in July and September of 2022 in Guangzhou, China. OOM-containing particles accounted for 57 % and 49 % of the total detected single particles in July and September, respectively. Most of the OOM particles were internally mixed with sulfate and nitrate, while elemental carbon and hydrocarbon species were absent. Despite the higher SOC/OC ratio in September (81 %) than it in July (72 %), comparative investigations of the mass spectra, diurnal patterns, and distributions of OOM particles revealed the same composition and aging states of OOMs in two O3 pollution periods. As the O3 concentration increased from the clean to the polluted periods, the ratio of SOC to OC increased along with the relative abundance of secondary OOM particles among total OOM particles. In contrast, the relative abundance of OC-type OOM particles gradually decreased, indicating the conversion of hydrocarbon species into OOMs as the SOC/OC ratio increased. Both the bulk analysis of SOC from filter measurement and the mixing states of OOM particles suggested that OOM production and degree of oxidation were higher in the O3-polluted periods than in the clean periods. These results elucidate the effects of O3 pollution on the OOM formation process and offer new perspectives for the joint investigation of SOA production based on filter sampling and single-particle measurements.

Enhancing traditional Chinese medicine diagnostics: Integrating ontological knowledge for multi-label symptom entity classification.

In traditional Chinese medicine (TCM), artificial intelligence (AI)-assisted syndrome differentiation and disease diagnoses primarily confront the challenges of accurate symptom identification and classification. This study introduces a multi-label entity extraction model grounded in TCM symptom ontology, specifically designed to address the limitations of existing entity recognition models characterized by limited label spaces and an insufficient integration of domain knowledge. This model synergizes a knowledge graph with the TCM symptom ontology framework to facilitate a standardized symptom classification system and enrich it with domain-specific knowledge. It innovatively merges the conventional bidirectional encoder representations from transformers (BERT) + bidirectional long short-term memory (Bi-LSTM) + conditional random fields (CRF) entity recognition methodology with a multi-label classification strategy, thereby adeptly navigating the intricate label interdependencies in the textual data. Introducing a multi-associative feature fusion module is a significant advancement, thereby enabling the extraction of pivotal entity features while discerning the interrelations among diverse categorical labels. The experimental outcomes affirm the model's superior performance in multi-label symptom extraction and substantially elevates the efficiency and accuracy. This advancement robustly underpins research in TCM syndrome differentiation and disease diagnoses.

Mixing states and secondary formation processes of organic nitrogen-containing single particles in Guangzhou, China.

Organic nitrogen (ON) compounds play a significant role in the light absorption of brown carbon and the formation of organic aerosols, however, the mixing state, secondary formation processes, and influencing factors of ON compounds are still unclear. This paper reports on the mixing state of ON-containing particles based on measurements obtained using a high-performance single particle aerosol mass spectrometer in January 2020 in Guangzhou. The ON-containing particles accounted for 21% of the total detected single particles, and the particle count and number fraction of the ON-containing particles were two times higher at night than during the day. The prominent increase in the content of ON-containing particles with the enhancement of NOx mainly occurred at night, and accompanied by high relative humidity and nitrate, which were associated with heterogeneous reactions between organics and gaseous NOx and/or NO3 radical. The synchronous decreases in ON-containing particles and the mass absorption coefficient of water-soluble extracts at 365 nm in the afternoon may be associated with photo-bleaching of the ON species in the particles. In addition, the positive matrix factorization analysis found five factors dominated the formation processes of ON particles, and the nitrate factor (33%) mainly contributed to the production of ON particles at night. The results of this study provide unique insights into the mixing states and secondary formation processes of the ON-containing particles.

Secondary aerosol formation in incense burning particles by O3 and OH oxidation via single particle mixing state analysis.

Incense burning is a common religious activity that emits abundant gaseous and particulate pollutants into the atmosphere. During their atmospheric lifetime, these gases and particles are subjected to oxidation, leading to the formation of secondary pollutants. We examined the oxidation of incense burning plumes under O3 exposure and dark condition using an oxidation flow reactor connected to a single particle aerosol mass spectrometer (SPAMS). Nitrate formation was observed in incense burning particles, mainly attributable to the ozonolysis of nitrogen-containing organic compounds. With UV on, nitrate formation was significantly enhanced, likely due to HNO3/HNO2/NOx uptake triggered by OH chemistry, which is more effective than ozone oxidation. The extent of nitrate formation is insensitive to O3 and OH exposure, possibly due to the diffusion limitation on interfacial uptake. The O3-UV-aged particles are more oxygenated and functionalized than O3-Dark-aged particles. Oxalate and malonate, two typical secondary organic aerosol (SOA) components, were found in O3-UV-aged particles. Our work reveals that nitrate, accompanied by SOA, can rapidly form in incense-burning particles upon photochemical oxidation in the atmosphere, which could deepen our understanding of air pollution caused by religious activities.

Long-Term Observation of Mixing States and Sources of Vanadium-Containing Single Particles from 2020 to 2021 in Guangzhou, China.

The distribution of vanadium (V) in aerosols is commonly used to track ship exhaust emissions, yet the atmospheric abundance of V has been greatly reduced due to the implementation of a clean fuel policy. Recent research mainly discussed the chemical compositions of ship-related particles during specific events, yet few studies focus on the long-term changes of V in the atmosphere. In this study, a single-particle aerosol mass spectrometer was used to measure V-containing particles from 2020 to 2021 in Huangpu Port in Guangzhou, China. The long-term trend of the particle counts of V-containing particles declined annually, but the relative abundance of V-containing particles in the total single particles increased in summer due to the influence of ship emissions. Positive matrix factorization revealed that in June and July 2020, 35.7% of the V-containing particles were from ship emissions, followed by dust and industrial emissions. Furthermore, more than 80% of the V-containing particles were found mixing with sulfate and 60% of the V-containing particles were found mixing with nitrate, suggesting that the majority of the V-containing particles were secondary particles processed during the transport of ship emissions to urban areas. Compared with the small changes in the relative abundance of sulfate in the V-containing particles, the relative abundance of nitrate exhibited clear seasonal variations, with a high abundance in winter. This may have been due to the increased production of nitrate from high concentrations of precursors and a suitable chemical environment. For the first time, the long-term trends of V-containing particles in two years are investigated to demonstrate changes in their mixing states and sources after the clean fuel policy, and to suggest the cautious application of V as an indicator of ship emissions.

Chemical characterization and source attribution of organic pollutants in industrial wastewaters from a Chinese chemical industrial park.

Accelerated urbanization and industrialization have led to an alarming increase in the generation of wastewater with complex chemical contents. Industrial wastewaters are often a primary source of water contamination. The chemical characterization of different industrial wastewater types is an essential task to interpret the chemical fingerprints of wastewater to identify pollution sources and develop efficient water treatment strategies. In this study, we conduct a non-target chemical analysis for the source characterization of different industrial wastewater samples collected from a chemical industrial park (CIP) located in southeast China. The chemical screening identified volatile and semi-volatile organic compounds that included dibutyl phthalate at a maximum concentration of 13.4 µg/L and phthalic anhydride at 35.9 µg/L. Persistent, mobile, and toxic (PMT) substances among the detected organic compounds were identified and prioritized as high-concern contaminants given their impact on drinking water resources. Moreover, a source analysis of the wastewater collected from the wastewater outlet station indicated that the dye production industry contributed the largest quantities of toxic contaminates (62.6%), and this result was consistent with the ordinary least squares and heatmap results. Thus, our study utilized a combined approach of a non-target chemical analysis, a pollution source identification method, and a PMT assessment of different industrial wastewater samples collected from the CIP. The results of the chemical fingerprints of different industrial wastewater types as well as the results of the PMT assessment benefit risk-based wastewater management and source reduction strategies.

Combining the External Medical Knowledge Graph Embedding to Improve the Performance of Syndrome Differentiation Model.

The electronic medical records (EMRs) of traditional Chinese medicine (TCM) include a wealth of TCM knowledge and syndrome diagnosis information, which is crucial for improving the quality of TCM auxiliary decision-making. In practical diagnosis, one disease corresponds to one syndrome, posing considerable hurdles for the informatization of TCM. The purpose of this work was to create an end-to-end TCM diagnostic model, and the knowledge graph (KG) created in this article is used to improve the model's information and realize auxiliary decision-making for TCM disorders. We approached auxiliary decision-making for syndrome differentiation in this article as a multilabel classification task and presented a knowledge-based decision support model for syndrome differentiation (KDSD). Specifically, we created a KG based on TCM features (TCMKG), supplementing the textual representation of medical data with embedded information. Finally, we proposed fusing medical text with KG entity representation (F-MT-KER) to get prediction results using a linear output layer. After obtaining the vector representation of the medical record text using the BERT model, the vector representation of various KG embedded models can provide additional hidden information to a certain extent. Experimental results show that our method improves by 1% (P@1) on the syndrome differentiation auxiliary decision task compared to the baseline model BERT. The usage of EMRs can aid TCM development more efficiently. With the help of entity level representation, character level representation, and model fusion, the multilabel classification method based on the pretraining model and KG can better simulate the TCM syndrome differentiation of the complex cases.

Emission factors and source profiles of volatile organic compounds from typical industrial sources in Guangzhou, China.

Volatile organic compounds (VOCs) are important precursors of ozone (O3) and fine particulate matter (PM2.5). An accurate depiction of the emission characteristics of VOCs is the key to formulating VOC control strategies. In this study, the VOC emission factors and source profiles in five industrial sectors were developed using large-scale field measurements conducted in Guangzhou, China (100 samples for the emission factors and 434 samples for the source profile measurements). The emission factors based on the actual measurement method and the material balance method were 1.6-152.4 kg of VOCs per ton of raw materials (kg/t) and 3.1-242.2 kg/t, respectively. The similarities between the emission factors obtained using these two methods were examined, which showed a coefficient of divergence (CD) of 0.34-0.72. Among the 33 subdivided VOC source profiles developed in this study, sources including light guide plate (LGP), photoresist mask, and plastic products were the first time developed in China. Due to regional diversities in terms of production technologies, materials, and products, the emission characteristics of the VOCs varied, even in the same sector, thereby demonstrating the importance of developing localized source profiles of VOCs. The ozone formation potential (OFP) of the shipbuilding and repair sector from fugitive emissions was the highest value among all the industrial sectors. Controlling the emissions of aromatics and OVOCs was critical to reducing the O3 growth momentum in industrial sectors. In addition, 1,2-dibromoethane showed high carcinogenic risk potentials (CRPs) during most of the industrial sectors and should be prioritized for controlling.