| 来源类型 | Working Papers |
| 规范类型 | 报告 |
| A Study of the Application of Spatio-Temporal Statistical Model of Particular Matter (PM10) Concentration in Seoul | |
| Hanwoom Hong | |
| 发表日期 | 2018-09-30 |
| 出版年 | 2018 |
| 语种 | 英语 ; Korean |
| 摘要 | As the social interest in particulate matter (PM10) increases, studies on particulate matter have been carried out in various fields, but statistical methodology has been used limitedly. PM10 data is spatio-temporal data having time dependence and spatial dependence simultaneously. In recent decades, statistical techniques for statistical analysis of spatio-temporal data have been developed in statistical field, and the results of study with respect to applying spatio-temporal statistics to PM10 data have been published. The purpose of this study is to predict the risk of PM10 concentration based on Seoul metropolitan city using the recent spatio-temporal statistical methods. First, we introduce the spatio-temporal statistical model and investigate its advantages and disadvantages compared with the physical model which is widely used for PM10 prediction. The challenging tasks in carrying out spatio-temporal statistical analysis can be the fact that time dependence and space dependence should be matched. The aforementioned time dependence and space dependence should be balanced so as to avoid overfitting or underfitting problems. Since the optimal time resolution for a given spatial resolution is not known yet, the actual analysis must be applied to various temporal resolutions. To that end, overseas case study applying the spatio-temporal statistical model to the air quality was examined and applied to PM10 of Seoul metropolitan city. The analytical coverage is based on the PM10 data of Seoul metropolitan city in 2016, and fit the model for 1 hour, 3 hours, and 8 hours time windows. The least stable data of April, the most stable of July and neutral of October were targeted to be analyzed. The results show that the proposed method is in good agreement with the prediction of maximum value and the prediction of VaR (Value at Risk). Spatio-temporal statistical models are suitable to expand to the national level and can be used for receptor-based research. It is expected that effective analysis will be possible if the observatory is located in the widespread agricultural and fishing villages. |
| 来源智库 | Korea Evironment Institute (Republic of Korea) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/198409 |
| 推荐引用方式 GB/T 7714 | Hanwoom Hong. A Study of the Application of Spatio-Temporal Statistical Model of Particular Matter (PM10) Concentration in Seoul. 2018. |
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| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| 기ì´_2018_06_íí（3466KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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