AIMC Topic: Particulate Matter
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Evaluating drivers of PM air pollution at urban scales using interpretable machine learning.
Waste management (New York, N.Y.)
Dec 1, 2024
Reducing urban fine particulate matter (PM) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the key drivers of PM will enable the development of targeted strategies to reduce PM levels. This stud...
Regional PM prediction with hybrid directed graph neural networks and Spatio-temporal fusion of meteorological factors.
Environmental pollution (Barking, Essex : 1987)
Nov 28, 2024
Traditional statistical prediction methods on PM often focus on a single temporal or spatial dimension, with limited consideration for regional transport interactions among adjacent cities. To address this limitation, we propose a hybrid directed gra...
A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications.
Environmental monitoring and assessment
Nov 27, 2024
Over time, computing power and storage resource advancements have enabled the widespread accumulation and utilization of data across various domains. In the field of air quality, analyzing data and developing air quality models have become pivotal in...
MTLPM: a long-term fine-grained PM2.5 prediction method based on spatio-temporal graph neural network.
Environmental monitoring and assessment
Nov 23, 2024
The concentration of PM2.5 is one of the air quality indicators that the public pays the most attention to. Existing methods for PM2.5 prediction primarily analyze and forecast data from individual monitoring stations, without considering the mutual ...
Spatiotemporal variations of PM and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning.
Environmental pollution (Barking, Essex : 1987)
Nov 22, 2024
Ozone pollution was widely reported along with PM reduction since 2013 in China. However, the meteorological drivers for ozone varying with different regions of China remains unknown using explainable machine learning, especially during the COVID-19 ...
Improving PM and PM predictions in China from WRF_Chem through a deep learning method: Multiscale depth-separable UNet.
Environmental pollution (Barking, Essex : 1987)
Nov 21, 2024
Accurate predictions of atmospheric particulate matter can be applied in providing services for air pollution prevention and control. However, the forecasting accuracy of traditional air quality models is limited owing to model uncertainties. In this...
Evaluation of machine learning and deep learning models for daily air quality index prediction in Delhi city, India.
Environmental monitoring and assessment
Nov 19, 2024
The air quality index (AQI), based on criteria for air contaminants, is defined to provide a shared vision of air quality. As air pollution continues to rise in global cities due to urbanization and climate change, air pollution monitoring and foreca...
High-resolution spatiotemporal prediction of PM concentration based on mobile monitoring and deep learning.
Environmental pollution (Barking, Essex : 1987)
Nov 18, 2024
Obtaining the high-resolution distribution characteristics of urban air pollutants is crucial for effective pollution control and public health. In order to fulfill it, mobile monitoring offers a novel and practical approach compared to traditional f...
Accurate and efficient prediction of atmospheric PM, PM, PM, and O concentrations using a customized software package based on a machine-learning algorithm.
Chemosphere
Nov 16, 2024
Particulate matter (PM) and ozone (O) pollution have been attracting increasing attention recently due to their severe harm to human health. PM and O are secondary pollutants, and there remain significant challenges in accurately and efficiently pred...
Improving the construction and prediction strategy of the Air Quality Health Index (AQHI) using machine learning: A case study in Guangzhou, China.
Ecotoxicology and environmental safety
Nov 9, 2024
Effectively capturing the risk of air pollution and informing residents is vital to public health. The widely used Air Quality Index (AQI) has been criticized for failing to accurately represent the non-threshold linear relationship between air pollu...
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