Emission sources, Characteristics and risk assessment of particulate bound Polycyclic Aromatic Hydrocarbons (PAHs) from traffic sites

Ambade, Balram; Kumar, Amit; Latif, Mohd Talib

doi:10.21203/rs.3.rs-328364/v1

Cited by 16 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address this issue, the adoption of clean-production methods using new energy sources such as photovoltaic and nuclear powers. Moreover, efforts should be made to reduce biomass combustion from coal, petroleum, farmland grass and wood, as well as to control traffic-related oil and gas emissions [ 69 , 70 ].…”

Section: Discussionmentioning

confidence: 99%

Historical sedimentary and evolutionary characteristics of POPs and EDCs in typical regions of the three Gorges reservoir, China

Dong,

Cao,

Pan

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Historical sedimentary and evolutionary characteristics of POPs and EDCs in typical regions of the three Gorges reservoir, China

Dong,

Cao,

Pan

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Source apportionment studies of measured air pollutants, such as black carbon, PM 2.5 , and PM 10 , have revealed that the major emission sources of these pollutants are influenced by factors including industrial production, vehicular exhaust, and biomass burning [59][60][61]. The spatial distribution of these different emission sources has varying impacts on regional air quality.…”

Section: Poi Datamentioning

confidence: 99%

Enhancing Air Quality Forecasting: A Novel Spatio-Temporal Model Integrating Graph Convolution and Multi-Head Attention Mechanism

Wang,

Liu,

et al. 2024

Atmosphere

View full text Add to dashboard Cite

Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for predicting air quality spatio-temporal variations is introduced. The model, named graph long short-term memory with multi-head attention (GLSTMMA), is designed to capture the temporal patterns and spatial relationships within multivariate time series data related to air quality. The GLSTMMA model utilizes a hybrid neural network architecture to effectively learn the complex dependencies and correlations present in the data. The extraction of spatial features related to air quality involves the utilization of a graph convolutional network (GCN) to collect air quality data based on the geographical distribution of monitoring sites. The resulting graph structure is imported into a long short-term memory (LSTM) network to establish a Graph LSTM unit, facilitating the extraction of temporal dependencies in air quality. Leveraging a Graph LSTM unit, an encoder-multiple-attention decoder framework is formulated to enable a more profound and efficient exploration of spatio-temporal correlation features within air quality time series data. The research utilizes the 2019–2021 multi-source air quality dataset of Qinghai Province for experimental assessment. The results indicate that the model effectively leverages the impact of multi-source data, resulting in optimal accuracy in predicting six air pollutants.

show abstract

“…Atmospheric pollutants fall onto the marine surface through dry deposition and wet deposition processes [33]. Dry deposition involves the physical, chemical, and biological processes by which atmospheric pollutants fall onto the marine surface, while wet deposition involves ionic pollutants and soluble pollutants from the air falling onto the marine surface through precipitation or water vapor condensation [34]. The evaluation of marine atmospheric pollution deposition is mainly conducted based on the "Technical Regulations for the Assessment of the Flux of Atmospheric Pollutants Deposition into the Sea (Trial)" and includes the observation of the elements nitrate, NH 4 Cl, Cu, Pb, and Zn.…”

Section: Sources and Monitoring Indices Of Marine Pollutionmentioning

confidence: 99%

A Global Review of Progress in Remote Sensing and Monitoring of Marine Pollution

Ma,

Pan

et al. 2023

Water

View full text Add to dashboard Cite

With the rapid development of urbanization and industrialization, human activities have caused marine pollution in three ways: land source, air source, and sea source, leading to the problem of marine environments. Remote sensing, with its wide coverage and fast and accurate monitoring capability, continues to be an important tool for marine environment monitoring and evaluation research. This paper focuses on the three types of marine pollution, namely marine seawater pollution, marine debris and microplastic pollution, and marine air pollution. We review the application of remote sensing technology methods for monitoring marine pollution and identify the limitations of existing methods. Marine seawater pollution can be effectively monitored by remote sensing technology, especially where traditional monitoring methods are inadequate. For marine debris and microplastic pollution, the monitoring methods are still in the early stages of development and require further research. For marine air pollution, more air pollution parameters are required for accurate monitoring. Future research should focus on developing marine remote sensing with data, technology, and standard sharing for three-dimensional monitoring, combining optical and physical sensors with biosensors, and using multi-source and multi-temporal monitoring data. A marine multi-source monitoring database is necessary to provide an immediately available basis for coastal and marine governance, improve marine spatial planning, and help coastal and marine protection.

show abstract

Emission sources, Characteristics and risk assessment of particulate bound Polycyclic Aromatic Hydrocarbons (PAHs) from traffic sites

Cited by 16 publications

References 24 publications

Historical sedimentary and evolutionary characteristics of POPs and EDCs in typical regions of the three Gorges reservoir, China

Historical sedimentary and evolutionary characteristics of POPs and EDCs in typical regions of the three Gorges reservoir, China

Enhancing Air Quality Forecasting: A Novel Spatio-Temporal Model Integrating Graph Convolution and Multi-Head Attention Mechanism

A Global Review of Progress in Remote Sensing and Monitoring of Marine Pollution

Contact Info

Product

Resources

About