利用单颗粒气溶胶质谱仪研究燃煤尘质谱特征

采用单颗粒气溶胶质谱仪(SPAMS)和再悬浮采样器联用的方式对燃煤电厂烟道气样品和下载灰样品的质谱特征进行测定,并使用颗粒物粒径分级采样仪ELPI测定其粒径分布特征.研究表明,SPAMS监测得到的粒径分布与ELPI结果差异较大,SPAMS对于500 nm以上粒径段检测效果较好;两个样品正谱图中有非常明显的锂、钙、钛、铝等金属组分信号和碳组分信号特征,负谱图中硅酸盐、硝酸盐和硫酸盐等信号比较明显,并且随着粒径的增加碳组分、硫酸盐和硝酸盐等组分对应的信号强度逐渐减弱,而硅酸盐、铝、钙和钛等组分对应的信号强度逐渐增强;对两个样品使用ART-2a聚类获得多个颗粒物类别,分析表明,它们均含有元素碳二次类(硫酸盐和硝酸盐缩写为二次Sec)、有机碳二次类、铝元素碳类、铝钙硅酸盐类和富硅酸盐类等颗粒物类别,并且随着粒径的增加金属硅酸盐颗粒出现频率增大,而含碳颗粒与硫酸盐出现频率降低.但烟道气样品和下载灰样品的质谱特征呈显著差异,下载灰样品更能代表燃煤源真实排放特征.建议在今后建立基于单颗粒质谱固定源成分谱时,应使用单颗粒气溶胶质谱仪在外场进行实测,并使用聚类的方法提取不同粒径段上的源质谱特征,可能会取得更好的效果.

Study on the source spectral characteristics of particles emitted from coal combustion by SPAMS

SPAMS and Re-suspension sampler were adopted to measure mass spectral signatures of fly ashes in stack gas and ashes captured by emission control devices of a power plant, and ELPI was used to analyze its size distribution. The size distributions of power plant emitted particles measured by SPAMS and ELPI are different, and SPAMS has better performance on particles larger than 500 nm. A significant amount of Li, Al, Ca and Ti were observed in the positive mass spectrum of both samples, while silicate, nitrate and sulfate peaked in the negative mass spectrum. As particle sizes increase, intensities of carbonaceous components, sulfate and nitrate decrease, but intensities of silicate, aluminum, calcium and titanium increase. Mass spectral features of two samples were extracted by ART-2a, and the results indicated that both of them contain elemental carbon-Sec (sulfate and nitrate are shorted as Sec), organic carbon-Sec, aluminum-elemental carbon, aluminum-calcium-silicate, and silicate rich classes. With the size growing, the appearing frequency of metal-silicate particles increase, while carbon-rich particles and sulfate-rich particles decrease. But, there are significant difference between mass spectral features of two samples, and stack gas samples are more representative of the real emission character. The results suggest in the future study to characterize chemical fingerprints for individual particles from emission sources, single particle mass spectrometry should be applied in the field to analyze in real time, and clustering method should be applied to extract spectrum characteristics in different size ranges for getting better results.