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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Hlavní autoři:	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
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2023-10-01
Edice:	Atmosphere
Témata:	PM<sub>10</sub>/PM<sub>2.5</sub> fraction soil erosion artificial neural network Monte Carlo Taklamakan Desert
On-line přístup:	https://www.mdpi.com/2073-4433/14/11/1644
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