scholarly journals Spatial dissimilarities in seasonal cycle of PM10 particulate matters in Seoul, Korea

2019 ◽  
Author(s):  
Parth Bansal

The increase in spatial and temporal resolution of pollution data has advanced the understanding of trend in pollutant concentration through time and space. However, this has also made inspection of time series and the relation between observations from different monitoring stations difficult to comprehend. In this study, we decompose PM10 concentration time series (2010 - 2018) from 40 Air Quality Monitoring Stations (AQMs) in Seoul and then cluster the seasonal cyclic components using K-Means and K-Shape algorithms. Firstly, the influence of diurnal, weekly and annual cycle on PM10 concentration is shown. Secondly, the clustering results illustrate that both algorithms are useful in determining AQMs with similar cycles, and together provide a comprehensive understanding of spatial differences in pollutant concentration.

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1096
Author(s):  
Edward Ming-Yang Wu ◽  
Shu-Lung Kuo

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.


2018 ◽  
Vol 111 ◽  
pp. 20-30 ◽  
Author(s):  
Maria Crăciun ◽  
Călin Vamoş ◽  
Nicolae Suciu

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