| Title |
Estimation of background PM2.5 concentrations for an air-polluted environment |
| Authors |
Wang, Sheng-Hsiang , Hung, Ruo-Ya , Lin, Neng-Huei , GÓMEZ LOSADA, ALVARO, Pires, Jose C. M. , Shimada, Kojiro , Hatakeyama, Shiro , Takami, Akinori |
| External publication |
Si |
| Means |
ATMOSPHERIC RESEARCH |
| Scope |
Article |
| Nature |
Científica |
| JCR Quartile |
1 |
| SJR Quartile |
1 |
| JCR Impact |
5.369 |
| SJR Impact |
1.488 |
| Publication date |
01/01/2020 |
| ISI |
000513178000001 |
| DOI |
10.1016/j.atmosres.2019.104636 |
| Abstract |
The background PM2.5 concentration represents the combined emissions from natural domestic and foreign sources, which has implications for the maximum effect, in terms of air-quality control, that can be achieved by reducing emissions. However, estimating the background PM2.5 concentration via background monitoring sites for a densely populated region (e.g., Taiwan) has been a challenge. In this study, we compared two statistical methods of estimating the background concentration using an 11-year time series (2005-2016) of data from three air-quality stations in Taiwan. The results of two methods showed good agreement for the background PM2.5 concentration estimation, which was about 4.4 mu g m(-3) and comparable to literature reports. According to the trend analysis, the concentration has decreased at a rate of 1-2 mu g m(-3) decade(-1) as a result of better emissions control in East Asia in recent years. Furthermore, the local concentration can exceed the regional background value by up to 5 times due to local emissions, topographic effects, and weather regimes. When considering the cross-county transport of PM2.5, a difference as high as 5 mu g m(-3) exists between two prevailingwind scenarios. This study provides crucial information to policy-makers on setting an achievable and reasonable goal for PM2.5 reduction. |
| Keywords |
Air-quality monitoring networks; Background level; Hidden Markov Model; PM2.5 concentration |
| Universidad Loyola members |
|
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