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[Application of ARIMA Model for Mid- and Long-term Forecasting of Ozone Concentration].
Li, Ying-Ruo; Han, Ting-Ting; Wang, Jun-Xia; Quan, Wei-Jun; He, Di; Jiao, Re-Guang; Wu, Jin; Guo, Heng; Ma, Zhi-Qiang.
Afiliação
  • Li YR; Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China.
  • Han TT; Key Laboratory of Atmospheric Chemistry, China Meteorological Administration, Beijing 100089, China.
  • Wang JX; Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, China Meteorological Administration, Beijing 100089, China.
  • Quan WJ; Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China.
  • He D; Key Laboratory of Atmospheric Chemistry, China Meteorological Administration, Beijing 100089, China.
  • Jiao RG; College of Environmental Science and Engineering, Peking University, Beijing 100871, China.
  • Wu J; Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, China Meteorological Administration, Beijing 100089, China.
  • Guo H; Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, China Meteorological Administration, Beijing 100089, China.
  • Ma ZQ; Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, China Meteorological Administration, Beijing 100089, China.
Huan Jing Ke Xue ; 42(7): 3118-3126, 2021 Jul 08.
Article em Zh | MEDLINE | ID: mdl-34212637
Ozone pollution has recently become a severe air quality issue in the Beijing-Tianjin-Hebei region. Due to the lack of a precursor emission inventory and complexity of physical and chemical mechanism of ozone generation, numerical modeling still exhibits significant deviations in ozone forecasting. Owing to its simplicity and low calculation costs, the time series analysis model can be effectively applied for ozone pollution forecasting. We conducted a time series analysis of ozone concentration at Shangdianzi, Baoding, and Tianjin sites. Both seasonal and dynamic ARIMA models were established to perform mid- and long-term ozone forecasting. The correlation coefficient R between the predicted and observed value can reach 0.951, and the RMSE is only 10.2 µg·m-3 for the monthly average ozone prediction by the seasonal ARIMA model. The correlation coefficient R between the predicted and observed value increased from 0.296-0.455 to 0.670-0.748, and RMSE was effectively reduced for the 8-hour ozone average predicted by the dynamic ARIMA model.
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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 2_ODS3 Base de dados: MEDLINE Assunto principal: Ozônio / Poluentes Atmosféricos / Poluição do Ar Tipo de estudo: Prognostic_studies / Risk_factors_studies País/Região como assunto: Asia Idioma: Zh Revista: Huan Jing Ke Xue Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 2_ODS3 Base de dados: MEDLINE Assunto principal: Ozônio / Poluentes Atmosféricos / Poluição do Ar Tipo de estudo: Prognostic_studies / Risk_factors_studies País/Região como assunto: Asia Idioma: Zh Revista: Huan Jing Ke Xue Ano de publicação: 2021 Tipo de documento: Article