Insight into PM2.5 Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing
Insight into PM2.5 Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing
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DOI：
10.5194/acp-2020-1017
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
D. Srivastava;Jingsha Xu;T. Vu;Di Liu;Linjie Li;P. Fu;S. Hou;Zongbo Shi;R. Harrison
中科院分区：
文献类型：
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作者：
D. Srivastava;Jingsha Xu;T. Vu;Di Liu;Linjie Li;P. Fu;S. Hou;Zongbo Shi;R. Harrison
抽象。本研究介绍了PMF在北京的城市（大气物理研究所- IAP）和农村站点（平谷-PG）收集的数据上进行的PM2.5源解析，作为中国特大城市大气污染与人类健康（APHH-北京）实地活动的一部分。活动于二零一六年十一月九日至十二月十一日及二零一七年五月二十二日至六月二十四日进行。PMF包括有机和无机两种物质，七因子输出为PM2.5源解析提供了最合理的解决方案。这些因素被解释为交通排放，生物质燃烧，道路粉尘，土壤粉尘，煤炭燃烧，石油燃烧和二次无机物。两个地点冬季PM2.5质量的主要贡献者是二次无机物（22- 24%）、生物质燃烧（30- 36%）和燃煤（20- 21%）。PG夏季PM2.5颗粒物主要由次生无机物（48%）、道路扬尘（20%）和燃煤（17%）组成，而IAP夏季PM2.5颗粒物主要由土壤扬尘（35%）和次生无机物（40%）组成。尽管如此，基于金属特征解决的因素并未完全解决，并表明两种或多种来源的混合。PMF结果还与从另一种受体模型（即CMB）解析的源进行了比较，并对其他测量（即在线和离线气溶胶质谱（AMS））进行了PMF，并对一些但不是所有源显示出良好的一致性。PMF中的生物质燃烧因子可能包含老化的气溶胶，因为在AMS中观察到生物质燃烧和含氧组分之间存在良好的相关性（r2 = 0.6-0.7）。PMF未能解决CMB和AMS确定的一些来源，似乎高估了尘埃源。与早期的PMF源解析研究从北京地区的比较突出了非常不同的结果，从应用这种方法。
Abstract. This study presents the source apportionment of PM2.5 performed by PMF on data collected at an urban (Institute of Atmospheric Physics – IAP) and a rural site (Pinggu-PG) in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-Beijing) field campaigns. The campaigns were carried out from 9th November to 11th December 2016 and 22nd May to 24th June 2017. The PMF included both organic and inorganic species, and a seven-factor output provided the most reasonable solution for the PM2.5 source apportionment. These factors are interpreted to be traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion and secondary inorganics. Major contributors to PM2.5 mass were secondary inorganics (22–24 %), biomass burning (30–36 %), and coal combustion (20–21 %) sources during the winter period at both sites. Secondary inorganics (48 %), road dust (20 %) and coal combustion (17 %) showed the highest contribution during summer at PG, while PM2.5 particles were mainly composed of soil dust (35 %) and secondary inorganics (40 %) at IAP. Despite this, factors that were resolved based on metal signatures were not fully resolved and indicate a mixing of two or more sources. PMF results were also compared with sources resolved from another receptor model (i.e. CMB) and PMF performed on other measurements (i.e. online and offline aerosol mass spectrometry (AMS)) and showed a good agreement for some but not all sources. The biomass burning factor in PMF may contain aged aerosols as a good correlation was observed between biomass burning and oxygenated fractions (r2 = 0.6–0.7) from AMS. The PMF failed to resolve some sources identified by the CMB and AMS, and appears to overestimate the dust sources. A comparison with earlier PMF source apportionment studies from the Beijing area highlights the very divergent findings from application of this method.