Predicting air quality using a quantitative forecasting model of PM2.5 micro-scale variation nested with wave spectrum analysis
This study focused on weather and environmental numerical prediction and public demand. It expanded the concept and technology growth points in new fields in terms of new tasks for major prediction services for “large-scale public events.” This is required for more advanced prediction and to improve...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Environmental Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2023.1232121/full |
| _version_ | 1797756069346279424 |
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| author | Deying Wang Jizhi Wang Yuanqin Yang Wenxing Jia Junting Zhong Yaqiang Wang Xiaoye Zhang |
| author_facet | Deying Wang Jizhi Wang Yuanqin Yang Wenxing Jia Junting Zhong Yaqiang Wang Xiaoye Zhang |
| author_sort | Deying Wang |
| collection | DOAJ |
| description | This study focused on weather and environmental numerical prediction and public demand. It expanded the concept and technology growth points in new fields in terms of new tasks for major prediction services for “large-scale public events.” This is required for more advanced prediction and to improve the resolution, fineness, and accuracy of the prediction. This study explored the prediction theory and technical application of transient atmospheric aerosol pollution within an accuracy of an hour. The novelty of this study is as follows: ①Based on high-quality big data covering the Northern Hemisphere with high temporal resolution with an accuracy of 1 h, a quantitative theory of the “natural weather cycle” spectral analysis algorithm was developed. This study presented a quantitative forecast model that nests the “spectral analysis of atmospheric wave-like disturbance” in the westerly belt with the “transient characteristics” of micro-scale aerosols (PM2.5 concentration) in Beijing and North China. ②According to the nested model of this study, the wave-like oscillation (H′) of 500 hPa was positively correlated with the PLAM index and PM2.5 mass concentration during nested multi-“natural weather cycles.” The significance level exceeded 0.001. This study demonstrated the prediction abilities of early quantitative fine prediction theory and implementation in the context of air quality. The forecast service on 1 October 2022, for the opening of the CCP 20th National Congress (16 October), and during the conference was successfully presented in real time. The results of this study on hourly resolution high-precision air quality forecasting service showed that rolling forecasts can be continuously released both 1 month and 7–10 days in advance, and the nesting effect can constantly be updated. Forecasts were found to be consistent with reality. ③The nested mode method for atmospheric spectrum analysis and micro-scale aerosol (PM2.5) distribution provides quantitative analysis and a decision-making basis for business-oriented operations to address technical difficulties. |
| first_indexed | 2024-03-12T17:56:14Z |
| format | Article |
| id | doaj.art-2b0b68754bd9412cb7ff75c64d0c9db6 |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-03-12T17:56:14Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj.art-2b0b68754bd9412cb7ff75c64d0c9db62023-08-02T14:58:45ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2023-08-011110.3389/fenvs.2023.12321211232121Predicting air quality using a quantitative forecasting model of PM2.5 micro-scale variation nested with wave spectrum analysisDeying WangJizhi WangYuanqin YangWenxing JiaJunting ZhongYaqiang WangXiaoye ZhangThis study focused on weather and environmental numerical prediction and public demand. It expanded the concept and technology growth points in new fields in terms of new tasks for major prediction services for “large-scale public events.” This is required for more advanced prediction and to improve the resolution, fineness, and accuracy of the prediction. This study explored the prediction theory and technical application of transient atmospheric aerosol pollution within an accuracy of an hour. The novelty of this study is as follows: ①Based on high-quality big data covering the Northern Hemisphere with high temporal resolution with an accuracy of 1 h, a quantitative theory of the “natural weather cycle” spectral analysis algorithm was developed. This study presented a quantitative forecast model that nests the “spectral analysis of atmospheric wave-like disturbance” in the westerly belt with the “transient characteristics” of micro-scale aerosols (PM2.5 concentration) in Beijing and North China. ②According to the nested model of this study, the wave-like oscillation (H′) of 500 hPa was positively correlated with the PLAM index and PM2.5 mass concentration during nested multi-“natural weather cycles.” The significance level exceeded 0.001. This study demonstrated the prediction abilities of early quantitative fine prediction theory and implementation in the context of air quality. The forecast service on 1 October 2022, for the opening of the CCP 20th National Congress (16 October), and during the conference was successfully presented in real time. The results of this study on hourly resolution high-precision air quality forecasting service showed that rolling forecasts can be continuously released both 1 month and 7–10 days in advance, and the nesting effect can constantly be updated. Forecasts were found to be consistent with reality. ③The nested mode method for atmospheric spectrum analysis and micro-scale aerosol (PM2.5) distribution provides quantitative analysis and a decision-making basis for business-oriented operations to address technical difficulties.https://www.frontiersin.org/articles/10.3389/fenvs.2023.1232121/fullwave spectrum analysisnatural weather cyclenested model of air quality forecastaerosol (PM2.5)precursor signals |
| spellingShingle | Deying Wang Jizhi Wang Yuanqin Yang Wenxing Jia Junting Zhong Yaqiang Wang Xiaoye Zhang Predicting air quality using a quantitative forecasting model of PM2.5 micro-scale variation nested with wave spectrum analysis Frontiers in Environmental Science wave spectrum analysis natural weather cycle nested model of air quality forecast aerosol (PM2.5) precursor signals |
| title | Predicting air quality using a quantitative forecasting model of PM2.5 micro-scale variation nested with wave spectrum analysis |
| title_full | Predicting air quality using a quantitative forecasting model of PM2.5 micro-scale variation nested with wave spectrum analysis |
| title_fullStr | Predicting air quality using a quantitative forecasting model of PM2.5 micro-scale variation nested with wave spectrum analysis |
| title_full_unstemmed | Predicting air quality using a quantitative forecasting model of PM2.5 micro-scale variation nested with wave spectrum analysis |
| title_short | Predicting air quality using a quantitative forecasting model of PM2.5 micro-scale variation nested with wave spectrum analysis |
| title_sort | predicting air quality using a quantitative forecasting model of pm2 5 micro scale variation nested with wave spectrum analysis |
| topic | wave spectrum analysis natural weather cycle nested model of air quality forecast aerosol (PM2.5) precursor signals |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2023.1232121/full |
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