数值模式及机器学习对兰州市近地面臭氧模拟适用性

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Abstract Numerical- and machine learning models with three different chemical mechanisms (CBMZ, RADM2, and CB06r3) were applied to simulate the near-surface ozone concentration in Lanzhou city in July 2019. Results show that CBMZ performed better than both RADM2 and CB06r3 did of which RADM2led to an overestimate of the near-surface ozone concentration while CB06r3 to a slightly underestimate. Then, the results from two machine learning models (XGBoost and PSO-BP) showed that in the absence of atmospheric pollutant emission inventory and only meteorological data were used, both two machine learning models performed better, regardless of single site or spatial distribution. In addition, the XGBoost model performed better for simulating the spatial distribution of near-surface ozone concentrations. Overall, the two machine learning models computed faster than the numerical models, but were significantly influenced by the input data, implying that the numerical models are more suitable for simulating pollution processes. Generally, a model suitable for simulating ground-level ozone should be selected according to the simulation requirements and data conditions.

Key words： ozone numerical model chemical mechanism machine learning

Received: 23 May 2023

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X511

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	Articles by authors
	ZHOU Heng-zuo
	LIAO Peng
	YANG Hong
	CHEN Heng-rui
	LUO Yi-ming
	PAN Feng
	TONG Ji-long
	LIU Yong-le

Cite this article:

ZHOU Heng-zuo,LIAO Peng,YANG Hong等. The applicability of numerical model and machine learning for near-surface ozone simulation in Lanzhou City[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(1): 15-27.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I1/15

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