To investigate the composition and possible sources of particles, especially during heavy haze pollution, a single particle aerosol mass spectrometer（SPAMS） was deployed to measure the changes of single particle spe...To investigate the composition and possible sources of particles, especially during heavy haze pollution, a single particle aerosol mass spectrometer（SPAMS） was deployed to measure the changes of single particle species and sizes during October of 2014, in Beijing. A total of 2,871,431 particles with both positive and negative spectra were collected and characterized in combination with the adaptive resonance theory neural network algorithm（ART-2a）. Eight types of particles were classified： dust particles（dust, 8.1%）, elemental carbon（EC, 29.0%）, organic carbon（OC, 18.0%）, EC and OC combined particles（ECOC, 9.5%）,Na-K containing particles（Na K, 7.9%）, K-containing particles（K, 21.8%）, organic nitrogen and potassium containing particles（KCN, 2.3%）, and metal-containing particles（metal,3.6%）. Three haze pollution events（P1, P2, P3） and one clean period（clean） were analyzed,based on the mass and number concentration of PM_（2.5）and the back trajectory results from the hybrid single particle Lagrangian integrated trajectory model（Hysplit-4 model）. Results showed that EC, OC and K were the major components of single particles during the three haze pollution periods, which showed clearly increased ratios compared with those in the clean period. Results from the mixing state of secondary species of different types of particles showed that sulfate and nitrate were more readily mixed with carbon-containing particles during haze pollution episodes than in clean periods.展开更多
基金supported by the National Natural Science Foundation of China (No.41205115)
文摘To investigate the composition and possible sources of particles, especially during heavy haze pollution, a single particle aerosol mass spectrometer（SPAMS） was deployed to measure the changes of single particle species and sizes during October of 2014, in Beijing. A total of 2,871,431 particles with both positive and negative spectra were collected and characterized in combination with the adaptive resonance theory neural network algorithm（ART-2a）. Eight types of particles were classified： dust particles（dust, 8.1%）, elemental carbon（EC, 29.0%）, organic carbon（OC, 18.0%）, EC and OC combined particles（ECOC, 9.5%）,Na-K containing particles（Na K, 7.9%）, K-containing particles（K, 21.8%）, organic nitrogen and potassium containing particles（KCN, 2.3%）, and metal-containing particles（metal,3.6%）. Three haze pollution events（P1, P2, P3） and one clean period（clean） were analyzed,based on the mass and number concentration of PM_（2.5）and the back trajectory results from the hybrid single particle Lagrangian integrated trajectory model（Hysplit-4 model）. Results showed that EC, OC and K were the major components of single particles during the three haze pollution periods, which showed clearly increased ratios compared with those in the clean period. Results from the mixing state of secondary species of different types of particles showed that sulfate and nitrate were more readily mixed with carbon-containing particles during haze pollution episodes than in clean periods.
