空气质量模拟与观测机器学习NO<sub>2</sub>浓度预报

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Abstract In this study, built upon the WRF-CMAQ air quality model simulations, a novel machine learning method based on simulations and observations (SOML) that integrating feedforward neural network (FNN) and long short-term Memory network (LSTM) through the Lasso method was developed for forecasting NO₂ concentrations, where LSTM was derived based on real-time pollutant and meteorological data. This innovative method was then applied to forecast the NO₂ concentrations for three consecutive days for ten air quality monitoring stations in Shunde, Foshan to evaluate the model performance. Our results show that: Compared to WRF-CMAQ and other individual models, SOML gave higher accuracy in the three-day forecast of NO₂ concentrations, with the mean absolute error (MAE) of first day at 4.99μg/m³, decreasing up to 66.18%; The accuracy of SOML predictions has significantly improved compared with that of WRF-CMAQ, indicating SOML’s suitable applicability to all seasons (MAE decreased by 42.18%, 42.89%, 61.04% and 50.91%, respectively), particularly in autumn and winter; and Compared with WRF-CMAQ, SOML appears to provide better forecasting accuracy of the spatial distribution as well as the NO₂ concentration levels at each station in Shunde.

Key words： NO₂ concentration forecast machine learning forecast model WRF-CMAQ model air quality monitoring

Received: 23 April 2023

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X511

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	HUANG Yong-xi
	ZHU Yun
	XIE Yang-hong
	LI Hai-xian
	ZHANG Zhi-cheng
	LI Jie
	LI Jin-ying
	YUAN Ying-zhi

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HUANG Yong-xi,ZHU Yun,XIE Yang-hong等. Forecast of NO₂ concentrations based on coupled air quality model simulations and monitoring data using machine learning method[J]. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(12): 6225-6234.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2023/V43/I12/6225

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