globalchange  > 气候减缓与适应
DOI: 10.1002/2017JD028052
Scopus记录号: 2-s2.0-85047462521
论文题名:
Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong
作者: Liu T.; Lau A.K.H.; Sandbrink K.; Fung J.C.H.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2018
卷: 123, 期:8
起始页码: 4175
结束页码: 4196
语种: 英语
英文关键词: air quality ; ARIMA ; forecast ; numerical model ; stochastic model ; time series
Scopus关键词: air quality ; atmospheric modeling ; comparative study ; forecasting method ; nitrous oxide ; numerical model ; ozone ; particulate matter ; pollution monitoring ; stochasticity ; time series analysis ; China ; Hong Kong
英文摘要: Based on prevailing numerical forecasting models (Community Multiscale Air Quality [CMAQ] model, Comprehensive Air Quality Model with Extensions, and Nested Air Quality Prediction Modeling System) and observations from monitoring stations in Hong Kong, we employ a set of autoregressive integrated moving average (ARIMA) models with numerical forecasts (ARIMAX) to improve the forecast of air pollutants including PM2.5, NO2, and O3. The results show significant improvements in multiple evaluation metrics for daily (1–3 days) and hourly (1–72 hr) forecast. Forecasts on daily 1-hr and 8-hr maximum O3 are also improved. For instance, compared with CMAQ, applying CMAQ-ARIMA reduces average root-mean-square errors (RMSEs) at all stations for daily average PM2.5, NO2, and O3 in the next 3 days by 14.3–21.0%, 41.2–46.3%, and 47.8–49.7%, respectively. For hourly forecasts in the next 72 hr, reductions in RMSEs brought by ARIMAX using CMAQ are 18.2% for PM2.5, 32.1% for NO2, and 36.7% for O3. Large improvements in RMSEs are achieved for nonrural PM2.5 and rural NO2 using ARIMAX with three numerical models. Dynamic hourly forecast shows that ARIMAX can be applied for forecast of 7- to 72-hr PM2.5, 4- to 72-hr NO2, and 4- to 6-hr O3. Besides applying ARIMAX for NO2, we recommend a mixed forecast strategy to ARIMAX for normal values of PM2.5 and O3 and employ numerical models for outputs above 75th percentile of historical observations. Our hybrid ARIMAX method can combine the advantage of ARIMA and numerical modeling to assist real-time air quality forecasting efficiently and consistently. ©2018. American Geophysical Union. All Rights Reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/114057
Appears in Collections:气候减缓与适应

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作者单位: Division of Environment and Sustainability, Hong Kong University of Science and Technology, Hong Kong; Civil and Environmental Engineering, Hong Kong University of Science and Technology, Hong Kong; Institute of Neuroinformatics, ETH Zurich, Zurich, Switzerland; Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong

Recommended Citation:
Liu T.,Lau A.K.H.,Sandbrink K.,et al. Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong[J]. Journal of Geophysical Research: Atmospheres,2018-01-01,123(8)
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