VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan
This study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan....
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/11/10/1096 |
| _version_ | 1797551058901270528 |
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| author | Edward Ming-Yang Wu Shu-Lung Kuo |
| author_facet | Edward Ming-Yang Wu Shu-Lung Kuo |
| author_sort | Edward Ming-Yang Wu |
| collection | DOAJ |
| description | This study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan. Before the verification analysis of the EGARCH model is conducted, the air quality data collected at the ten air quality monitoring stations in the Kaohsiung–Pingtung area are classified into three major factors using the factor analyses in multiple statistical analyses. The factors with the most significance are then selected as the targets for conducting investigations; they are termed “photochemical pollution factors”, or factors related to pollution caused by air pollutants, including particulate matter with particles below 10 microns (PM<sub>10</sub>), ozone (O<sub>3</sub>) and nitrogen dioxide (NO<sub>2</sub>). Then, we applied the Vector Autoregressive Moving Average-EGARCH (VARMA-EGARCH) model under the condition where the standardized residual existed in order to study the relationships among three air pollutants and how their concentration changed in the time series. By simulating the optimal model, namely VARMA (1,1)-EGARCH (1,1), we found that when O<sub>3</sub> was the dependent variable, the concentration of O<sub>3</sub> was not affected by the concentration of PM<sub>10</sub> and NO<sub>2</sub> in the same term. In terms of the impact response analysis on the predictive power of the three air pollutants in the time series, we found that the asymmetry effect of NO<sub>2</sub> was the most significant, meaning that NO<sub>2</sub> influenced the GARCH effect the least when the change of seasons caused the NO<sub>2</sub> concentration to fluctuate; it also suggested that the concentration of NO<sub>2</sub> produced in this area and the degree of change are lower than those of the other two air pollutants. This research is the first of its kind in the world to adopt a VARMA-EGARCH model to explore the interplay among various air pollutants and reactions triggered by it over time. The results of this study can be referenced by authorities for planning air quality total quantity control, applying and examining various air quality models, simulating the allowable increase in air quality limits, and evaluating the benefit of air quality improvement. |
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| format | Article |
| id | doaj.art-49bd68b4f50d451abbf09632772b7da2 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T15:39:21Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-49bd68b4f50d451abbf09632772b7da22023-11-20T16:59:06ZengMDPI AGAtmosphere2073-44332020-10-011110109610.3390/atmos11101096VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern TaiwanEdward Ming-Yang Wu0Shu-Lung Kuo1College of Maritime, National Kaohsiung University of Science and Technology, Kaohsiung 811, TaiwanDepartment of Technology Management, The Open University of Kaohsiung, Kaohsiung 806, TaiwanThis study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan. Before the verification analysis of the EGARCH model is conducted, the air quality data collected at the ten air quality monitoring stations in the Kaohsiung–Pingtung area are classified into three major factors using the factor analyses in multiple statistical analyses. The factors with the most significance are then selected as the targets for conducting investigations; they are termed “photochemical pollution factors”, or factors related to pollution caused by air pollutants, including particulate matter with particles below 10 microns (PM<sub>10</sub>), ozone (O<sub>3</sub>) and nitrogen dioxide (NO<sub>2</sub>). Then, we applied the Vector Autoregressive Moving Average-EGARCH (VARMA-EGARCH) model under the condition where the standardized residual existed in order to study the relationships among three air pollutants and how their concentration changed in the time series. By simulating the optimal model, namely VARMA (1,1)-EGARCH (1,1), we found that when O<sub>3</sub> was the dependent variable, the concentration of O<sub>3</sub> was not affected by the concentration of PM<sub>10</sub> and NO<sub>2</sub> in the same term. In terms of the impact response analysis on the predictive power of the three air pollutants in the time series, we found that the asymmetry effect of NO<sub>2</sub> was the most significant, meaning that NO<sub>2</sub> influenced the GARCH effect the least when the change of seasons caused the NO<sub>2</sub> concentration to fluctuate; it also suggested that the concentration of NO<sub>2</sub> produced in this area and the degree of change are lower than those of the other two air pollutants. This research is the first of its kind in the world to adopt a VARMA-EGARCH model to explore the interplay among various air pollutants and reactions triggered by it over time. The results of this study can be referenced by authorities for planning air quality total quantity control, applying and examining various air quality models, simulating the allowable increase in air quality limits, and evaluating the benefit of air quality improvement.https://www.mdpi.com/2073-4433/11/10/1096air pollutant control areaair pollutantsphoto chemical pollution factorimpact response analysesmultiple statistical analyses |
| spellingShingle | Edward Ming-Yang Wu Shu-Lung Kuo VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan Atmosphere air pollutant control area air pollutants photo chemical pollution factor impact response analyses multiple statistical analyses |
| title | VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan |
| title_full | VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan |
| title_fullStr | VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan |
| title_full_unstemmed | VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan |
| title_short | VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan |
| title_sort | varma egarch model for air quality analyses and application in southern taiwan |
| topic | air pollutant control area air pollutants photo chemical pollution factor impact response analyses multiple statistical analyses |
| url | https://www.mdpi.com/2073-4433/11/10/1096 |
| work_keys_str_mv | AT edwardmingyangwu varmaegarchmodelforairqualityanalysesandapplicationinsoutherntaiwan AT shulungkuo varmaegarchmodelforairqualityanalysesandapplicationinsoutherntaiwan |