煤燃烧产物
环境科学
环境化学
硫酸盐
海盐
总有机碳
硝酸盐
微粒
气溶胶
生物质燃烧
燃烧
污染物
分摊
空气污染
环境工程
化学
法学
有机化学
政治学
作者
Wing Sze Chow,X. H. Hilda Huang,Ka Fung Leung,Lin Huang,Xiangrong Wu,Jian Zhen Yu
标识
DOI:10.1016/j.scitotenv.2021.152652
摘要
Source apportionment of PM2.5 was performed using positive matrix factorization (PMF) based on chemical speciation data from 24-h filters collected throughout 2015 at six sampling sites of varying urban influences in Hong Kong. The input data include major inorganic ions, organic and elemental carbon, elements, and organic tracers. Nine factors were resolved, including (1) secondary sulfate formation process, (2) secondary nitrate formation process, (3) industrial emissions, (4) biomass burning, (5) primary biogenic emissions, (6) vehicle emissions, (7) residual oil combustion, (8) dust, and (9) aged sea salt. The PMF-resolved factor contributions in conjunction with air mass back trajectories showed that the two major sources for PM2.5 mass, secondary sulfate (annual: 41%) and secondary nitrate (annual: 9.9%), were dominantly associated with regional and super-regional pollutant transport. Vehicular emissions are the most important local source, and its contributions exhibit a clear spatial variation pattern, with the highest (6.9 μg/m3, 24% of PM2.5) at a downtown roadside location and the lowest (0.4 μg/m3, 2.0% PM2.5) at two background sites away from city centers. The ability of producing a more reliable source separation and identifying new sources (e.g. primary biogenic source in this study) was a direct advantageous result of including organic tracers in the PMF analysis. PMF analysis conducted on the same dataset in this study but without including the organic tracers failed to separate the biomass burning emissions and industrial/coal combustion emissions. PMF analysis without the organic tracers would also over-apportion the contribution of vehicular emissions to PM2.5, which would bias the evaluation of the effectiveness of vehicle-related control measures. This work demonstrates the importance of organic markers in achieving more comprehensive and less biased source apportionment results.
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