Organic compound source profiles of PM2.5 from traffic emissions, coal combustion, industrial processes and dust

Tian, Yingze; Liu, Xiao; Huo, Ruiqing; Shi, Zongbo; Sun, Yueming; Feng, Yinchang; Harrison, Roy M.

doi:10.1016/j.chemosphere.2021.130429

Cited by 40 publications

(17 citation statements)

References 48 publications

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“…(1) For the missing values of the data we obtained, the value of the monitoring station with the largest correlation coefficient is used to fill in (2) The frequency of data collected by the atmospheric monitoring station is 30 seconds, but due to network delays and other reasons, the data interval actually stored in the database is not strictly 30 seconds, resulting in messy data. So, we resample the data and adjust the time interval to 10 minutes to ensure the regularity of the dataset…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Scientists use PM2.5 concentration to indicate the content of such particles per cubic meter of air; the higher the value, the more serious the air pollution. The main sources are industrial fuels, dust, motor vehicle exhaust, photochemical smog, and other pollutants [2]. Although fine particulate matter is only a small component of the earth's atmosphere, it has an important impact on air quality and visibility.…”

Section: Introductionmentioning

confidence: 99%

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PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks

Zeng

Wang

Chen

et al. 2021

Wireless Communications and Mobile Computing

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Industrial parks are one of the main sources of air pollution; the ability to forecast PM2.5, the main pollutant in the industrial park, is of great significance to the health of the workers in the industrial park and environmental governance, which can improve the decision-making ability of environmental management. Most of the existing PM2.5 concentration forecast methods lack the ability to model the dynamic temporal and spatial correlations of PM2.5 concentration. In an industrial park environment, in order to improve the accuracy of PM2.5 concentration forecast, based on deep learning technology, this paper proposes a spatiotemporal graph convolutional network based on the attention mechanism (STAM-STGCN) to solve the PM2.5 concentration forecast problem. When constructing the adjacency matrix, we not only use the Euclidean distance between sites but also consider the impact of wind fields and the impact of pollution sources near the nodes. In the process of model construction, we first use the spatiotemporal attention mechanism to capture the dynamic spatiotemporal correlations in PM2.5 data. In the spatiotemporal convolution module, we use graph convolutional neural networks to capture spatial features and standard convolution to describe temporal features. Finally, the output module adjusts the output shape of the data to produce the final forecast result. In this paper, the mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are used as the performance evaluation metrics of the model, and the Dongmingnan Industrial Park atmospheric dataset is used to verify the effectiveness of the proposed algorithm. The experimental results show that our STAM-STGCN model can more fully capture the spatial-temporal characteristics of PM2.5 concentration data; compared with the most advanced model in the comparison model, the RMSE can be improved about 24.2%, the MAE is improved about 35.8%, and the MAPE is improved about 34.6%.

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Section: Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks

Zeng

Wang

Chen

et al. 2021

Wireless Communications and Mobile Computing

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“…Polycyclic aromatic hydrocarbons (PAHs) are widespread environmental pollutants with various adverse effects on human health. People are exposed to these compounds mainly through airborne emissions during incomplete combustion of organic materials, such as fossil fuels from industrial sources, automobile exhaust and, cigarette smoke, as well as through their intake with food [ 1 , 2 , 3 ]. Among hundreds of known organic compounds belonging to the PAHs group, the Environmental Protection Agency identified 16 PAHs as high-priority pollutants.…”

Section: Introductionmentioning

confidence: 99%

Concentration of Polycyclic Aromatic Hydrocarbons (PAHs) in Human Serum and Adipose Tissues and Stimulatory Effect of Naphthalene in Adipogenesis in 3T3-L1 Cells

Mlyczyńska

Bongrani

Ramé

et al. 2023

IJMS

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Polycyclic aromatic hydrocarbons (PAHs) are one of the most prevalent classes of environmental pollutants. Some evidence shows that PAHs could be involved in human obesity. However, little is known about the distribution patterns of PAHs in human adipose tissue (AT) and the role of PAHs on adipogenesis/lipogenesis. The aims of this pilot study were to determine concentrations of 16 PAHs defined as high-priority pollutants in the plasma and adipose tissue of French and Polish bariatric patients, as well as their correlation with body mass index (BMI), plasma and AT adipokines expression levels. We finally investigated the role of naphthalene on cell proliferation, viability, and differentiation in 3T3-L1 preadipocytes. The concentration of most PAHs was similar in the three types of AT and it was significantly higher in AT as compared to plasma, suggesting bioaccumulation. Polish patients had higher PAH levels in AT than French ones. Only the concentration of naphthalene in AT was positively correlated with the BMI and serum or adipose chemerin, adiponectin and resistin expression, in French but not in Polish patients, who had significantly higher BMIs. Moreover, naphthalene exposure increased the cell proliferation of 3T3-L1 preadipocytes and lipogenesis, and increased the expression of genes involved in adipogenesis after cell differentiation. Taken together, PAHs and more particularly naphthalene could be an obesogenic molecule and increase the risk of obesity.

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“…The higher the value, the more serious the air pollution is. Its main sources are industrial fuel, dust, motor vehicle exhaust, photochemical smog and other pollutants (Tian et al, 2021). Although fine particulate matter is only a very small component of the Earth's atmosphere, it has an important impact on air quality and visibility.…”

Section: Introductionmentioning

confidence: 99%

For the aged: A novel PM2.5 concentration forecasting method based on spatial-temporal graph ordinary differential equation networks in home-based care parks

Zeng

Wang

Chen

et al. 2022

Front. Environ. Sci.

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The immune ability of the elderly is not strong, and the functions of the body are in a stage of degeneration, the ability to clear PM2.5 is reduced, and the cardiopulmonary system is easily affected. Accurate prediction of PM2.5 can provide guidance for the travel of the elderly, thereby reducing the harm of PM2.5 to the elderly. In PM2.5 prediction, existing works usually used shallow graph neural network (GNN) and temporal extraction module to model spatial and temporal dependencies, respectively, and do not uniformly model temporal and spatial dependencies. In addition, shallow GNN cannot capture long-range spatial correlations. External characteristics such as air humidity are also not considered. We propose a spatial-temporal graph ordinary differential equation network (STGODE-M) to tackle these problems. We capture spatial-temporal dynamics through tensor-based ordinary differential equation, so we can build deeper networks and exploit spatial-temporal features simultaneously. In addition, in the construction of the adjacency matrix, we not only used the Euclidean distance between the stations, but also used the wind direction data. Besides, we propose an external feature fusion strategy that uses air humidity as an auxiliary feature for feature fusion, since air humidity is also an important factor affecting PM2.5 concentration. Finally, our model is evaluated on the home-based care parks atmospheric dataset, and the experimental results show that our STGODE-M can more fully capture the spatial-temporal characteristics of PM2.5, achieving superior performance compared to the baseline. Therefore, it can provide better guarantee for the healthy travel of the elderly.

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Organic compound source profiles of PM2.5 from traffic emissions, coal combustion, industrial processes and dust

Cited by 40 publications

References 48 publications

PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks

PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks

Concentration of Polycyclic Aromatic Hydrocarbons (PAHs) in Human Serum and Adipose Tissues and Stimulatory Effect of Naphthalene in Adipogenesis in 3T3-L1 Cells

For the aged: A novel PM2.5 concentration forecasting method based on spatial-temporal graph ordinary differential equation networks in home-based care parks

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