空气质量模拟与观测机器学习NO2浓度预报

黄泳熙; 朱云; 谢阳红; 李海贤; 张志诚; 黎杰; 李金盈; 袁颖枝

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中国环境科学 ›› 2023, Vol. 43 ›› Issue (12) : 6225-6234.

大气污染与控制

空气质量模拟与观测机器学习NO₂浓度预报

黄泳熙¹, 朱云¹, 谢阳红², 李海贤², 张志诚¹, 黎杰¹, 李金盈¹, 袁颖枝¹

作者信息 +

Forecast of NO₂ concentrations based on coupled air quality model simulations and monitoring data using machine learning method

HUANG Yong-xi¹, ZHU Yun¹, XIE Yang-hong², LI Hai-xian², ZHANG Zhi-cheng¹, LI Jie¹, LI Jin-ying¹, YUAN Ying-zhi¹

Author information +

文章历史 +

摘要

在空气质量模拟预报数据基础上,采用套索算法(Lasso)将前馈神经网络(FNN)与基于污染物浓度及气象实时观测值搭建的长短期记忆网络(LSTM)组合,形成了模拟与观测机器学习(SOML)预报模型,开展了佛山市顺德区NO₂未来3d 10个镇街空气质量监测点位逐日浓度预报.结果显示:SOML3d的准确性均优于WRF-CMAQ及其它单一模型,其中第一天SOML平均绝对误差(MAE)为4.99 μg/m³,改进幅度达66.18%;SOML不同季节适用性均较强,四季预报效果均较WRF-CMAQ明显提升(MAE分别降低42.18%、42.89%、61.04%、50.91%),其中秋冬季改善幅度更好;相比WRF-CMAQ,SOML预报结果能较好反映顺德区内各站点NO₂浓度实际空间分布和数值水平,有效提升了浓度预报精准度.

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.

导出引用

黄泳熙, 朱云, 谢阳红, 李海贤, 张志诚, 黎杰, 李金盈, 袁颖枝. 空气质量模拟与观测机器学习NO₂浓度预报[J]. 中国环境科学. 2023, 43(12): 6225-6234

HUANG Yong-xi, ZHU Yun, XIE Yang-hong, LI Hai-xian, ZHANG Zhi-cheng, LI Jie, LI Jin-ying, YUAN Ying-zhi. Forecast of NO₂ concentrations based on coupled air quality model simulations and monitoring data using machine learning method[J]. China Environmental Science. 2023, 43(12): 6225-6234

中图分类号： X511

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