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.
周恒左, 廖鹏, 杨宏, 陈恒蕤, 落义明, 潘峰, 仝纪龙, 刘永乐. 数值模式及机器学习对兰州市近地面臭氧模拟适用性[J]. 中国环境科学, 2024, 44(1): 15-27.
ZHOU Heng-zuo, LIAO Peng, YANG Hong, CHEN Heng-rui, LUO Yi-ming, PAN Feng, TONG Ji-long, LIU Yong-le. The applicability of numerical model and machine learning for near-surface ozone simulation in Lanzhou City. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(1): 15-27.
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