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Fine particulate matter (PM 2.5) pollution is still one of China's most important environmental issues, especially in northern cities during wintertime. In this study, intensive real-time measurement campaigns were conducted in Xi'an, Shijiazhuang, and Beijing to investigate the chemical characteristics and source contributions of PM 2.5 and explore the formation of heavy pollution for policy implications. The chemical compositions of PM 2.5 in the three cities were all dominated by organic aerosol (OA) and nitrate (NO 3-). Results of source apportionment analyzed by a hybrid environmental receptor model (HERM) showed that the secondary formation source contributed more to PM 2.5 compared to other primary sources. Biomass burning was the dominant primary source in the three pilot cities. The contribution of coal combustion to PM 2.5 is non-negligible in Xi'an and Shijiazhuang but is no longer an important contributor in the capital city of Beijing due to the execution of a strict coal-banning policy. The potential formation mechanisms of secondary aerosol in the three cities were further explored by establishing the correlations between the secondary formation sources and aerosol liquid water content (ALWC) and Ox (O3+NO2) , respectively. The results showed that photochemical oxidation and aqueous-phase reaction were two important pathways of secondary aerosol formation. According to source variations, air pollution events that occurred in campaigns were classified into three types: biomass-combustion-dominated, secondary-formation-source-dominated, and a combination of primary and secondary sources. Additionally, this study compares the changes in chemical composition and source contributions of PM 2.5 in past decades. The results suggest that the clean-energy replacements for rural households should be urgently encouraged to reduce the primary source emissions in northern China, and collaborative control on ozone and particulate matter needs to be continuously promoted to weaken the atmosphere oxidation capacity for the sake of reducing secondary aerosol formation. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) pollution is still one of China's most important environmental issues, especially in northern cities during wintertime. In this study, intensive real-time measurement campaigns were conducted in Xi'an, Shijiazhuang, and Beijing to investigate the chemical characteristics and source contributions of PM2.5 and explore the formation progress of heavy pollution for policy implications. The chemical compositions of PM2.5 in the three cities were all dominated by organic aerosol (OA) and nitrate (NO3-). Results of source apportionment analyzed by the hybrid environmental receptor model (HERM) showed that the secondary nitrate plus sulfate contributed higher to PM2.5 compared to other primary sources. Biomass burning was the dominant primary source in the three pilot cities. The contribution of coal combustion to PM2.5 is non-negligible in Xi'an and Shijiazhuang but is no longer the important contributor in the capital city of Beijing due to the execution of a strict coal-banning policy. The potential formation mechanisms of secondary aerosol in three cities were further explored by establishing the correlations between the secondary nitrate plus sulfate and aerosol liquid water content (ALWC), and O x (O3 + NO2), respectively. The results showed that photochemical oxidation and aqueous-phase reaction were two important pathways of secondary aerosol formation. According to the source variations, air pollution events that occurred in campaigns were classified into three types: biomass combustion dominated, secondary nitrate plus sulfate dominated, and a combination of primary and secondary sources. Additionally, this study compared the changes in chemical composition and source contributions of PM2.5 in past decades. The results suggested that the clean energy replacements for the rural household should be urgently encouraged to reduce the primary source emissions in northern China, and collaborative control on ozone and particulate matter need to be continuously promoted to weaken the atmosphere oxidation capacity for the sake of reducing secondary aerosol formation. [ABSTRACT FROM AUTHOR]

A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a 1-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zurich, Switzerland. The measured organic aerosol mass spectra were analyzed by PMF using a small (14 d) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainties of the PMF solutions were estimated. Factor–tracer correlations for averaged seasonal results from the rolling window analysis are higher than those retrieved from conventional PMF analyses of individual seasons, highlighting the improved performance of the rolling window algorithm for long-term data. In this study four to five factors were tested for every PMF window. Factor profiles for primary organic aerosol from traffic (HOA), cooking (COA) and biomass burning (BBOA) were constrained. Secondary organic aerosol was represented by either the combination of semi-volatile and low-volatility organic aerosol (SV-OOA and LV-OOA, respectively) or by a single OOA when this separation was not robust. This scheme led to roughly 40 000 PMF runs. Full visual inspection of all these PMF runs is unrealistic and is replaced by predefined user-selected criteria, which allow factor sorting and PMF run acceptance/rejection. The selected criteria for traffic (HOA) and BBOA were the correlation with equivalent black carbon from traffic (eBC tr) and the explained variation of m/z 60, respectively. COA was assessed by the prominence of a lunchtime concentration peak within the diurnal cycle. SV-OOA and LV-OOA were evaluated based on the fractions of m/z 43 and 44 in their respective factor profiles. Seasonal pre -tests revealed a non-continuous separation of OOA into SV-OOA and LV-OOA, in particular during the warm seasons. Therefore, a differentiation between four-factor solutions (HOA, COA, BBOA and OOA) and five-factor solutions (HOA, COA, BBOA, SV-OOA and LV-OOA) was also conducted based on the criterion for SV-OOA. HOA and COA contribute between 0.4–0.7 µgm-3 (7.8 %–9.0 %) and 0.7–1.2 µgm-3 (12.2 %–15.7 %) on average throughout the year, respectively. BBOA shows a strong yearly cycle with the lowest mean concentrations in summer (0.6 µgm-3 , 12.0 %), slightly higher mean concentrations during spring and fall (1.0 and 1.5 µgm-3 , or 15.6 % and 18.6 %, respectively), and the highest mean concentrations during winter (1.9 µgm-3 , 25.0 %). In summer, OOA is separated into SV-OOA and LV-OOA, with mean concentrations of 1.4 µgm-3 (26.5 %) and 2.2 µgm-3 (40.3 %), respectively. For the remaining seasons the seasonal concentrations of SV-OOA, LV-OOA and OOA range from 0.3 to 1.1 µgm-3 (3.4 %–15.9 %), from 0.6 to 2.2 µgm-3 (7.7 %–33.7 %) and from 0.9 to 3.1 µgm-3 (13.7 %–39.9 %), respectively. The relative PMF errors modeled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average ±34% , ±27% , ±30% , ±11% , ±25% and ±12% , respectively. [ABSTRACT FROM AUTHOR]