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首页> 外文期刊>Environmental Pollution >Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison: A case study in hangzhou, China
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Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison: A case study in hangzhou, China

机译：通过传统大气模型和机器学习揭示对流层臭氧及其比较：以中国杭州为例

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摘要

Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enough to decode it for better regulation. Therefore, we use the traditional atmospheric model, Weather Research and Forecasting coupled with Community Multi-scale Air Quality (WRF-CMAQ), and machine learning models, Extreme Learning Machine (ELM), Multi-layer Perceptron (MLP), Random Forest (RF) and Recurrent Neural Network (RNN) to analyze and predict the ozone in the surface air in Hangzhou, China, using meteorology and air pollutants as input. We firstly quantitatively demonstrate that the dew-point deficit, instead of temperature and relative humidity, is the predominant meteorological factor in shaping tropospheric ozone. Urban heat island, daily direct solar radiation time, wind speed and wind direction play trivial role in impacting tropospheric ozone. NO2 is the primary influential factors both for hourly ozone and daily O-3-8 h due to the titration effect. The most environmental-friendly way to mitigate the ozone pollution is to lower the volatile organic compounds (VOCs) with the highest ozone formation potentials. We deduce that the tropospheric ozone formation process tends to be not only non-linear but also non-smooth. Compared with the traditional atmospheric models, machine learning, whose characteristics are rapid convergence, short calculating time, adaptation of forecasting episodes, small program memory, higher accuracy and less cost, is able to predict tropospheric ozone more accurately. (C) 2019 Elsevier Ltd. All rights reserved.
机译：近年来，地表空气中的对流层臭氧已成为中国杭州的主要大气污染物。先前的分析不足以对其进行解码以实现更好的调节。因此，我们使用传统的大气模型，天气研究和预报以及社区多尺度空气质量（WRF-CMAQ），以及机器学习模型，极限学习机（ELM），多层感知器（MLP），随机森林（ RF）和循环神经网络（RNN），以气象和空气污染物为输入，分析和预测中国杭州地表空气中的臭氧。我们首先定量证明露点不足而不是温度和相对湿度是形成对流层臭氧的主要气象因素。城市热岛，每日直接太阳辐射时间，风速和风向在影响对流层臭氧方面起着微不足道的作用。由于滴定作用，NO 2是每小时臭氧和每天O-3-8 h的主要影响因素。减轻臭氧污染最环保的方法是降低具有最高臭氧形成潜能的挥发性有机化合物（VOC）。我们推论出对流层臭氧的形成过程不仅趋向于非线性而且趋于非平稳。与传统的大气模型相比，机器学习具有收敛速度快，计算时间短，预测事件适应性强，程序存储器小，精度高，成本低等特点，能够更准确地预测对流层臭氧。 （C）2019 Elsevier Ltd.保留所有权利。

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  • 来源
    《Environmental Pollution》 |2019年第1期|366-378|共13页
  • 作者

    Feng Rui; Zheng Hui-jun; Zhang An-ran; Huang Chong; Gao Han; Ma Yu-cheng;

  • 作者单位

    Zhejiang Univ State Key Lab Clean Energy Utilizat 38 Zheda Rd Hangzhou 310027 Zhejiang Peoples R China;

    Zhejiang Univ Sch Med Sir Run Run Shaw Hosp Dept Intens Care Unit Hangzhou 310020 Zhejiang Peoples R China;

    Zhejiang Tongji Vocat Coll Sci & Technol Hangzhou 311215 Zhejiang Peoples R China;

    Hangzhou Netease Zaigu Technol Co Ltd Hangzhou 310052 Zhejiang Peoples R China;

    Zhejiang Construct Investment Environm Engn Co Lt Hangzhou 310013 Zhejiang Peoples R China;

    Changan Univ Sch Elect & Control Engn Middle Sect Nan Er Huan Xian 710064 Shaanxi Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Recurrent neural network; Random forest; Multi-layer perceptron; WRF-CMAQ; Feature importance;

    机译：递归神经网络随机森林;多层感知器WRF-CMAQ;功能重要性;

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