VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan

Edward Ming-Yang Wu,Shu-Lung Kuo

doi:10.3390/atmos11101096

Abstract

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 (PM10), ozone (O3) and nitrogen dioxide (NO2). 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 O3 was the dependent variable, the concentration of O3 was not affected by the concentration of PM10 and NO2 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 NO2 was the most significant, meaning that NO2 influenced the GARCH effect the least when the change of seasons caused the NO2 concentration to fluctuate; it also suggested that the concentration of NO2 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.

Highlights

According to air quality monitoring reports from the Environmental Protection Administration, major pollutants causing the Pollutant Standard Index (PSI) [1] to exceed the air quality standard are particulate matter (PM10 ) and ozone (O3 )
(VARMA-Exponential Generalized Autoregressive Conditional Heteroscedasticity (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 O3 was the dependent variable, the concentration of O3 was not affected by the concentration of PM10 and NO2 in the same term

Summary

Introduction

According to air quality monitoring reports from the Environmental Protection Administration, major pollutants causing the Pollutant Standard Index (PSI) [1] to exceed the air quality standard are particulate matter (PM10 ) and ozone (O3 ). Taiwan’s air quality data are released to the public in the form of pollution standards index (PSI) values, following the procedure identical to that of the United States Environmental Protection Agency. Atmosphere 2020, 11, 1096 of five air pollutants: particulate matter with particles below 10 microns (PM10 ), sulfur dioxide (SO2 ), nitrogen dioxide (NO2 ), carbon monoxide (CO), and ozone (O3 ). Since air is highly dispersive or can be transported long range, if the total emission tolerance of pollutants in one area is not kept below that area’s capacity, the pollutants will influence the air quality in that area, and in adjacent regions [4,5]

Methods

Results

Conclusion