兰州市CMAQ近地面臭氧模拟结果的订正方法——基于机器学习方法

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Abstract To better simulate the near-surface ozone concentration in Lanzhou, the XGBoost (eXtreme Gradient Boosting) model and the LSTM (Long and Short-Term Memory) neural network model in the machine learning method were used on the basis of CMAQ (The Community Multiscale Air Quality Modeling System) to establish a revised model of near-surface ozone simulation results, and the combined LSTM-XGBoost model was constructed based on the two methods with the combination of the inverse of error variance weights to further improve the revision effect. In this study, four national monitoring sites in Lanzhou (Lan Lian Hotel, Railway Design Institute, Yuzhong Campus, and Biological Products Institute) were selected, and ambient air quality monitoring data and meteorological data in July and August 2019 were used to revise the near-surface ozone concentrations simulated by CMAQ. Results showed that the CMAQ model could simulate the spatial and temporal distributions of the near-surface ozone concentrations in Lanzhou, but the concentrations was underestimated. Among the revised models mentioned above, the XGBoost combined model revised best. Compared with the simulation results of CMAQ, the correlation of ozone concentration improved from 0.61~0.76 to 0.89~0.95, the correlation of 8h ozone concentration improved from 0.65~0.79 to 0.81~0.88, the ozone RMSE improved from 44.83~70.17μg/m³ to 15.21~26.53μg/m³, 8h ozone RMSE improved from 40.07~67.57μg/m³ to 14.24~28.54μg/m³. This study indicated that it is feasible to revise the model simulation results using machine learning methods to improve the air quality model.

Key words： CMAQ ozone machine learning LSTM XGBoost error-variant weighted inverse combination

Received: 13 May 2022

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X511

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	Articles by authors
	ZHOU Heng-zuo
	CHEN Heng-rui
	LIAO Peng
	KONG Xiang-rui
	PAN Feng
	YANG Hong

Cite this article:

ZHOU Heng-zuo,CHEN Heng-rui,LIAO Peng等. A revised approach to CMAQ near-surface ozone modelling results in Lanzhou-Based on machine learning methods[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(12): 5472-5483.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I12/5472

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