Soil-Derived Dust PM<sub>10</sub> and PM<sub>2.5</sub> Fractions in Southern Xinjiang, China, Using an Artificial Neural Network Model

Soil-Derived Dust PM<sub>10</sub> and PM<sub>2.5</sub> Fractions in Southern Xinjiang, China, Using an Artificial Neural Network Model

Soil-derived dust emissions have been widely associated with health and environmental problems and should therefore be accurately and reliably estimated and assessed. Of these emissions, the inhalable PM<sub>10</sub> and PM<sub>2.5</sub> are difficult to estimate. Consequentl...

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Bibliographic Details
Main Authors: Shuang Gao, Yaxin Liu, Jieqiong Zhang, Jie Yu, Li Chen, Yanling Sun, Jian Mao, Hui Zhang, Zhenxing Ma, Wen Yang, Ningning Hong, Merched Azzi, Hong Zhao, Hui Wang, Zhipeng Bai
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Atmosphere
Subjects:
PM<sub>10</sub>/PM<sub>2.5</sub> fraction
soil erosion
artificial neural network
Monte Carlo
Taklamakan Desert
Online Access:https://www.mdpi.com/2073-4433/14/11/1644

Internet

https://www.mdpi.com/2073-4433/14/11/1644

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