Soil-Derived Dust PM₁₀ and PM_2.5 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 PM₁₀ and PM_2.5 are difficult to estimate. Consequently, to calculate PM<sub>1...

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Bibliografiske detaljer

Principais autores:	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:	Artigo
Sprog:	Inglês
Udgivet:	MDPI AG 2023-10-01
Serier:	Atmosphere
Fag:	PM<sub>10</sub>/PM<sub>2.5</sub> fraction soil erosion artificial neural network Monte Carlo Taklamakan Desert
Online adgang:	https://www.mdpi.com/2073-4433/14/11/1644
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