Estimating representative background PM2.5 concentration in heavily polluted areas using baseline separation technique and chemical mass balance model. (February 2018)

• Record Type: Journal Article
• Authors: Gao, Shuang; Yang, Wen; Zhang, Hui; Sun, Yanling; Mao, Jian; Ma, Zhenxing; Cong, Zhiyuan; Zhang, Xian; Tian, Shasha; Azzi, Merched; Chen, Li; Bai, Zhipeng
• Is Part Of: Atmospheric environment. Volume 174(2018)
• Page Start: 180
• Page End: 187
• Publication Date: 2018-02
• Subjects: Background concentration -- PM2.5 -- Air pollutant -- Time-series -- Baseline separation -- Chemical mass balance model; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2017.11.045
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: The determination of background concentration of PM2.5 is important to understand the contribution of local emission sources to total PM2.5 concentration. The purpose of this study was to exam the performance of baseline separation techniques to estimate PM2.5 background concentration. Five separation methods, which included recursive digital filters (Lyne-Hollick, one-parameter algorithm, and Boughton two-parameter algorithm), sliding interval and smoothed minima, were applied to one-year PM2.5 time-series data in two heavily polluted cities, Tianjin and Jinan. To obtain the proper filter parameters and recession constants for the separation techniques, we conducted regression analysis at a background site during the emission reduction period enforced by the Government for the 2014 Asia-Pacific Economic Cooperation (APEC) meeting in Beijing. Background concentrations in Tianjin and Jinan were then estimated by applying the determined filter parameters and recession constants. The chemical mass balance (CMB) model was also applied to ascertain the effectiveness of the new approach. Our results showed that the contribution of background PM concentration to ambient pollution was at a comparable level to the contribution obtained from the previous study. The best performance was achieved using the Boughton two-parameter algorithm. The background concentrations were estimated at (27 ± 2) μg/m 3 for the whole year, (34 ± 4) μg/m 3 for the heating period (winter), (21 ± 2) μg/m 3 for the non-heating period (summer), and (25 ± 2) μg/m 3 for the sandstorm period in Tianjin. The corresponding values in Jinan were (30 ± 3) μg/m 3, (40 ± 4) μg/m 3, (24 ± 5) μg/m 3, and (26 ± 2) μg/m 3, respectively. The study revealed that these baseline separation techniques are valid for estimating levels of PM2.5 air pollution, and that our proposed method has great potential for estimating the background level of other air pollutants. Highlights: Background concentration of PM2.5 in heavily polluted cities in China is studied. Background concentration of PM2.5 is estimated by baseline separation techniques. Results from baseline separation techniques are validated by CMB model. Boughton two parameter algorithm is a proper method for the background estimation.