Spatiotemporal variations of ambient PM&amp;lt;sub&amp;gt;10&amp;lt;/sub&amp;gt; source contributions in Beijing in 2004 using positive matrix factorization

Xie, Shaodong; Liu, Zhen; Chen, T.; Hua, Long

doi:10.5194/acp-8-2701-2008

Cited by 50 publications

(31 citation statements)

References 35 publications

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“…Beijing, the capital of China, is one of the largest mega cities in the world, with approximately 17 million inhabitants and over 3.2 million motor vehicles to date (∼5% diesel vehicles in 2004; Xie et al, 2008). Any efforts to improve the air quality of Beijing will require a clear understanding of the potential sources of particles.…”

Section: Motivationmentioning

confidence: 99%

Traffic restrictions in Beijing during the Sino-African Summit 2006: aerosol size distribution and visibility compared to long-term in situ observations

et al. 2008

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Abstract. Based on the long-term in-situ observations of aerosol particle number size distributions and meteorological parameters, the measures of traffic restriction during the Sino-African Summit (4-6 November 2006) in Beijing, China have been found to be efficient in reducing the number concentration of aerosol particles, in particular Aitken and accumulation mode particles, and in improving the visibility when local emissions dominated. The influence of traffic restrictions on the particle concentrations differed for different particle sizes. More significant effects on fine particles with diameters ranging from 40 to 500 nm have been found. Based on statistical analysis of long-term observations, under comparable weather conditions, the number concentrations of the particles in Aitken and accumulation modes seemingly were reduced by 20-60% when the traffic restrictions were in place. This change may be mainly due to the reduction of secondary particle contributions. However, it is worth to notice that the reduction of 60% might overestimate the effect of the measures of traffic control, due to the inherent data shortage with very high wind speeds in the comparison data population. Our size-dependent aerosol data also indicate that measures led to reductions in particulate air pollution in the optically most important diameter range, whereas further vehicle restriction measures might lead to an increase in ultrafine particle formation if the condensational sink further decreased. Assuming that there were no traffic restrictions and with normal levels of the vehicle emissions, the visCorrespondence to: J. Heintzenberg (jost@tropos.de) ibilities during the Summit would have been lower by about 20-45%. The fact that over 95% cases with visibility range lower than 5 km during 2004 to 2007 occurred when the local wind speed was lower than 3 m s −1 may suggest that the importance of the emission restrictions is highest when the wind speed is lower than 3 m s −1 , concerning the improvement of serious low visibility situations in Beijing. MotivationParticles in the atmosphere arise from natural sources and from anthropogenic activities. They are emitted directly as particles or formed in the atmosphere by gas-to-particle conversions. A significant fraction of the tropospheric aerosol is anthropogenic in origin (Seinfeld and Pandis, 1998), in particular with diameters below one micrometer (fine particles) which result from nucleation/condensation of anthropogenic particle precursors or as directly emitted soot particles. Many urban regions face serious particulate pollution problems and their environmental agencies need to understand the most important aerosol sources before implementing clean-air measures.A forward strategy in trying to understand the relationships between different pollution sources and air quality starts with information on emissions and then predicts the pollution by means of air quality models with different emission scenarios. An inverse research strategy uses the atmosphere as a natural labor...

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Section: Motivationmentioning

confidence: 99%

Traffic restrictions in Beijing during the Sino-African Summit 2006: aerosol size distribution and visibility compared to long-term in situ observations

et al. 2008

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“…Lim et al (2010) measured PM 10 concentration at Daejeon, Korea and identified nine different sources using PMF model that includes vehicle exhaust, soil, road dust, cement/construction, fossil fuel combustion and field burning, secondary aerosol, incineration/Pb related industry and metal smelting. Xie et al (2008) determined source contributions to ambient PM 10 in Beijing, China with PMF model. Results indicated various sources of ambient PM 10 which were urban fugitive dust, crustal soil, coal combustion, secondary sulfate and nitrate, biomass burning with municipal incineration and vehicle emissions.…”

Section: Source Apportionmentmentioning

confidence: 99%

Source Apportionment of PM10 at an Urban Site of a South Asian Mega City

Shahid

Alvi

Shahid

et al. 2018

Aerosol Air Qual. Res.

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In the present study, analysis and source apportionment of the elemental composition of PM 10 was conducted in the urban atmosphere of Karachi. Trace elements such as Ni, Ba, Cd, Ca, Mg, Cr, Mn, Fe, Co, Cu, Sr and Ti were measured. The PM 10 concentration ranged from 255 μg m -3 to 793 μg m -3 , with an average of 438 ± 161 μg m -3 . Among the various elements analyzed, concentrations of Ca, Al and Fe were the highest (> 10000 ng m -3 ), followed by Mg and S (> 1000 ng m -3 ). Elements such as Zn, P, Cu, Pb, Mn, Ti, Sr and Ba demonstrated medium concentrations (> 100 ng m -3 ), whereas the lowest concentrations were found for elements such as Cr, Ni and Se (> 10 ng m -3 ). The Positive Matrix Factorization (PMF) model identified five possible factors that contributed to PM 10 , namely, biomass burning, coal combustion, resuspended road/soil dust, vehicular emissions and industrial dust. Industrial dust was the highest contributor (23.2%) to PM 10 followed by Biomass burning (23%), Vehicular emissions (22.2%), Coal combustion (21.7%) and Re-suspended dust (9.9%). A strong positive correlation (R 2 = 0.98) was observed between the model predicted PM 10 mass and the gravimetrically measured mass collected on filters.

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“…PMF is a multivariate factor analysis tool that decomposes a matrix of speciated sample data into two matrices-factor contributions and factor profiles that can be interpreted by an analyst as to what sources are represented based on observations at the receptor site (Guo et al, 2010;Ou et al, 2015;Shao et al, 2016;Shi et al, 2009;Xie et al, 2008;Lanz et al, 2007;Zhang et al, 2013). The object function Q, based on the uncertainties inherent in each observation, can allow the analyst to review the distribution for each species to evaluate the stability of the solution:…”

Section: Pmf Model Analysismentioning

confidence: 99%

The levels, variation characteristics, and sources of atmospheric non-methane hydrocarbon compounds during wintertime in Beijing, China

Liu

et al. 2017

Atmos. Chem. Phys.

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Abstract. Atmospheric non-methane hydrocarbon compounds (NMHCs) were measured at a sampling site in Beijing city from 15 December 2015 to 14 January 2016 to recognize their pollution levels, variation characteristics, and sources. We quantified 53 NMHCs, and the proportions of alkanes, alkenes, acetylene, and aromatics to the total NMHCs were 49. 8-55.8, 21.5-24.7, 13.5-15.9, and 9.3-10.7 %, respectively. The variation trends in the NMHC concentrations were basically identical and exhibited remarkable fluctuation, which was mainly ascribed to the variation in meteorological conditions, especially wind speed. The diurnal variations in NMHCs on clear days exhibited two peaks during the morning and evening rush hours, whereas the rush hours' peaks diminished or even disappeared on the haze days, implying that the relative contribution of the vehicular emissions to atmospheric NMHCs depended on the pollution status. Two evident peaks of the propane / propene ratios appeared in the early morning before sun rise and at noontime on clear days, whereas only one peak occurred in the afternoon during the haze days, which were attributed to the relatively fast reactions of propene with OH, NO 3 , and O 3 . Based on the chemical kinetic equations, the daytime OH concentrations were calculated to be in the range of 3.47 × 10 5 -1.04 × 10 6 molecules cm −3 on clear days and 6.42×10 5 -2.35×10 6 molecules cm −3 on haze days. The nighttime NO 3 concentrations were calculated to be in the range of 2.82 × 10 9 -4.86 × 10 9 molecules cm −3 on clear days. The correlation coefficients of typical hydrocarbon pairs (benzene / toluene, o-xylene / m,p-xylene, isopentane / n-pentane, etc.) revealed that vehicular emissions and coal combustion were important sources for atmospheric NMHCs in Beijing during the wintertime. Five major emission sources for atmospheric NMHCs in Beijing during the wintertime were further identified by positive matrix factorization (PMF), including gasoline-related emissions (gasoline exhaust and evaporation), coal combustion, diesel exhaust, acetylene-related emissions, and consumer and household products. Coal combustion (probably domestic coal combustion) was found to make the greatest contribution (29.6-33.4 %) to atmospheric NMHCs during haze days.

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Spatiotemporal variations of ambient PM<sub>10</sub> source contributions in Beijing in 2004 using positive matrix factorization

Cited by 50 publications

References 35 publications

Traffic restrictions in Beijing during the Sino-African Summit 2006: aerosol size distribution and visibility compared to long-term in situ observations

Traffic restrictions in Beijing during the Sino-African Summit 2006: aerosol size distribution and visibility compared to long-term in situ observations

Source Apportionment of PM10 at an Urban Site of a South Asian Mega City

The levels, variation characteristics, and sources of atmospheric non-methane hydrocarbon compounds during wintertime in Beijing, China

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