Estimation of background PM2.5 concentrations for an air-polluted environment

Sheng Hsiang Wang, Ruo Ya Hung, Neng Huei Lin, Álvaro Gómez-Losada, José C.M. Pires, Kojiro Shimada, Shiro Hatakeyama, Akinori Takami

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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 μg m−3 and comparable to literature reports. According to the trend analysis, the concentration has decreased at a rate of 1–2 μg 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 μg m−3 exists between two prevailing-wind scenarios. This study provides crucial information to policy-makers on setting an achievable and reasonable goal for PM2.5 reduction.

Original languageEnglish
Article number104636
JournalAtmospheric Research
StatePublished - 1 Jan 2020


  • Air-quality monitoring networks
  • Background level
  • Hidden Markov Model
  • PM concentration


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