Abstract
This study aims to critically evaluate the source apportionment of fine particles by multiple receptor modelling approaches, including carbon mass balance modelling of filter-based radiocarbon (14C) data, Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) analysis on filter-based chemical speciation data, and PMF analysis on Aerosol Mass Spectrometer (AMS-PMF) or Aerosol Chemical Speciation Monitor (ACSM-PMF) data. These data were collected as part of the APHH-Beijing (Atmospheric Pollution and Human Health in a Chinese Megacity) field observation campaigns from 10th November to 12th December in winter 2016 and from 22nd May to 24th June in summer 2017. 14C analysis revealed the predominant contribution of fossil fuel combustion to carbonaceous aerosols in winter compared with non-fossil fuel sources, which is supported by the results from other methods. An extended Gelencsér (EG) method incorporating 14C data, as well as the CMB and AMS/ACSM-PMF methods, generated a consistent source apportionment for fossil fuel related primary organic carbon. Coal combustion, traffic and biomass burning POC were comparable for CMB and AMS/ACSM-PMF. There are uncertainties in the EG method when estimating biomass burning and cooking OC. The POC from cooking estimated by different methods was poorly correlated, suggesting a large uncertainty when differentiating this source type. The PM2.5 source apportionment results varied between different methods. Through a comparison and correlation analysis of CMB, PMF and AMS/ACSM-PMF, the CMB method appears to give the most complete and representative source apportionment of Beijing aerosols. Based upon the CMB results, fine aerosols in Beijing were mainly secondary inorganic ion formation, secondary organic aerosol formation, primary coal combustion and from biomass burning emissions.
Highlights
Fine particulate matter (PM2.5) has adverse effects on atmospheric visibility and human health, and in uences the climate.[1,2] In Beijing, PM2.5 pollution remains a major challenge with its hourly concentration reaching as high as 438 mg mÀ3 during the APHH-Beijing (Atmospheric Pollution and Human Health in a Chinese Megacity) winter campaign.[3]
The Primary OC from fossil fuel combustion (POCf) estimated by the extended Gelencser (EG) method was consistent with that estimated by Chemical Mass Balance (CMB) and AMS/ACSM-Positive Matrix Factorization (PMF), but SOC was much higher for the EG method than the others, and Primary OC from non-fossil sources (POCnf) was lower for the EG method than for CMB and AMS/ACSM-PMF
The results of the organic carbon (OC) source apportionment intercomparison are summarized here: (1) The reconstructed OC from all apportioned sources was comparable for CMB, the EG method and AMS/ACSM-PMF, but lower for PMF, which is due to the inability of PMF to model heavily polluted events and separate POC and SOC
Summary
Fine particulate matter (PM2.5) has adverse effects on atmospheric visibility and human health, and in uences the climate.[1,2] In Beijing, PM2.5 pollution remains a major challenge with its hourly concentration reaching as high as 438 mg mÀ3 during the APHH-Beijing (Atmospheric Pollution and Human Health in a Chinese Megacity) winter campaign.[3] Source apportionment of PM2.5 provides important information for developing more effective pollution control strategies Receptor modelling methods such as Positive Matrix Factorization (PMF), Chemical Mass Balance (CMB) and UNMIX have been widely applied for the source apportionment of PM2.5.4 For the CMB model, aerosol chemical composition data from the sources and the receptor site are needed. CMB has been applied in many studies and has been con rmed to be a good tool for apportioning primary sources of carbonaceous aerosols.[6,7]
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