| 有机分子示踪物对大气PM2.5受体来源解析的影响：以杭州亚运会为例 |
| 摘要点击 1066 全文点击 145 投稿时间：2024-04-05 修订日期：2024-05-20 |
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| 中文关键词 PM2.5来源解析 正定矩阵因子分解法（PMF） 无机组分 有机分子示踪物 |
| 英文关键词 PM2.5 source apportionment positive matrix factorization （PMF） inorganic species organic molecular markers |
| 作者 | 单位 | E-mail | | 朱书慧 | 上海市环境科学研究院, 上海 200233
国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233 | zhush@saes.sh.cn | | 黄聪艳 | 上海市环境科学研究院, 上海 200233
国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233 | | | 来勇 | 浙江省杭州生态环境监测中心, 杭州 310007 | | | 吴宇航 | 上海市环境科学研究院, 上海 200233
国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233 | | | 严仁嫦 | 浙江省杭州生态环境监测中心, 杭州 310007 | | | 沈建东 | 浙江省杭州生态环境监测中心, 杭州 310007 | | | 田俊杰 | 上海市环境科学研究院, 上海 200233
国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233 | | | 高雅琴 | 上海市环境科学研究院, 上海 200233
国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233 | | | 马英歌 | 上海市环境科学研究院, 上海 200233
国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233 | mayg@saes.sh.cn |
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| 中文摘要 |
| 正定矩阵因子分解法（PMF）是目前国内外PM2.5源解析研究中应用最为广泛的受体模型之一,但传统PMF源解析方法大多数是基于硝酸盐、硫酸盐和元素碳等无机组分,源示踪性较弱,难以精细化解析大气PM2.5来源. 基于PM2.5中主要化学组分、痕量元素以及有机分子示踪物在线观测数据,采用PMF受体模型,全面对比了基于无机化学组分传统源解析方法（MCC）与基于有机分子示踪物源解析方法（OMM）在源类识别、源谱分离以及源贡献定量方面的差异. 结果表明,OMM方法对PM2.5来源解析的精细和精准程度有明显提升. 源识别方面,OMM方法通过输入多环芳烃、脂肪酸、二羧酸、羟基羧酸、C9酸和邻-苯二甲酸等有机分子示踪物,在MCC方法获得的工业源、船舶排放源、扬尘源、移动源、生物质燃烧源、二次硝酸盐和二次硫酸盐这7类源因子基础上,还解析获得了燃煤燃烧源、餐饮一次排放源以及4类二次有机气溶胶（SOA）源因子. 源谱分离方面,OMM方法能显著优化对不同燃烧排放源和SOA源的分离和识别,其解析获得的工业源、移动源、生物质燃烧源和燃煤燃烧源源谱特征与源排放实测结果较MCC方法更为吻合. 源贡献方面,MCC方法对移动源和生物质燃烧源贡献的定量总体高于OMM方法,尤其是当ρ（O3）较高（> 120 μg·m-3）时,MCC方法缺少有机分子示踪物的输入,无法较好地分离燃烧排放源和SOA源,导致解析获得的移动源和生物质燃烧源中包含了部分二次源贡献. 运用OMM方法,对亚运会前后杭州大气PM2.5来源变化开展进一步分析,发现会期移动源、工业源和扬尘源等一次源贡献较会前有明显下降,分别下降了65%、24%和24%,另外,由于挥发性有机物（VOCs）等前体物排放的削减,人为源SOA和高氧化态SOA源贡献在会期也有明显降低,分别降低了35%和49%,表明会期针对机动车排放、工业排放和工地扬尘等管控措施对于降低杭州PM2.5一次和二次生成有显著成效,揭示了有机分子示踪物在线观测数据对于精细化解析大气PM2.5来源、支撑下一阶段我国PM2.5污染精准防控的重要性. |
| 英文摘要 |
| Positive matrix factorization （PMF） is one of the most widely used receptor models for PM2.5 source apportionment. The traditional PMF method generally uses inorganic （such as nitrate, sulfate, and EC） measurement data as input species to apportion PM2.5 sources. These species have ambiguous source origins； thus quantifying PM2.5 sources with high source sectors is difficult. In this study, online measurements of major chemical components, elemental tracers, as well as organic molecular markers were applied in a PMF model to investigate the differences between the major chemical components-based PMF （MCC） and organic molecular markers-based PMF （OMM） methods in terms of source identification, source separation, and source quantification. The results showed that with the input of organic molecular markers （such as polycyclic aromatic hydrocarbons, fatty acids, dicarboxylic acids, hydroxyl-dicarboxylic acids, C9 acids, and phthalic acid）, the OMM method greatly enlarged the number of source factors identified. Further, industry emission, shipping emission, dust, vehicle emission, biomass burning, secondary nitrate, and secondary sulfate； two primary source factors （coal combustion and cooking emission）； and four secondary organic aerosol （SOA） source factors were also resolved in the OMM method. Comparing with the source profiles resolved by the MCC method, we found that OC/EC mass ratios in OMM-resolved source profiles of industry emission, vehicle emission, biomass burning, and coal combustion were closer to those obtained from emission inventories. In terms of source quantification, the mass contributions of vehicle emission and biomass burning resolved by the MCC method were notably higher than those resolved by the OMM method, especially under high O3 concentrations （> 120 μg·m-3）. This suggests that without the input of specific organic molecular markers, the MCC method was inclined to apportion parts of secondary source contributions into primary sources （such as vehicle emission and biomass burning）. We further quantified and compared PM2.5 source contributions in Hangzhou before, during, and after the 19th Asian Games with the application of the OMM method. Our results showed that the percentage contributions of vehicle emission, industry emission, and dust dropped by 65%, 24%, and 24%, respectively, during the Games. Anthropogenic SOA and aged SOA also displayed significant decreases in mass contributions during the Games by 35% and 49%, respectively, due to the emission reduction of volatile organic compounds （VOCs）. These results imply that PM2.5 pollution can be effectively controlled with the implementation of emission reduction measures. Our study also revealed that online measurements of organic molecular markers are important for improving PM2.5 source apportionment results and formulating pollution control policies. |
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