Characteristics of atmospheric organic and elemental carbon aerosols in urban Beijing, China

Ji, Dongsheng; Zhang, Junke; He, Jun; Wang, Xiaoju; Pang, Bo; Liu, Zirui; Wang, Lili; Wang, Yuesi

doi:10.1016/j.atmosenv.2015.11.020

Cited by 114 publications

(59 citation statements)

References 60 publications

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“…For comparison, OC and EC measurements by other Sunset semicontinuous carbon analyzers reported in the literature are summarized in Table . OC and EC concentrations observed in this study are comparable to other Chinese mega cities like Shanghai (Chang et al, ; Xu et al, ), Hong Kong (Zhou et al, ), and Nanjing (Chen et al, ) but lower than Beijing (Ji et al, ; Lin et al, ). OC and EC at NC are much higher compared to U.S. urban areas (Polidori et al, ; Rattigan et al, ), highlighting the severe air pollution situation of the PRD region.…”

Section: Resultssupporting

confidence: 83%

Estimation and Uncertainty Analysis of Secondary Organic Carbon Using 1 Year of Hourly Organic and Elemental Carbon Data

Wang

2019

JGR Atmospheres

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Hourly particulate matter 2.5 organic carbon (OC) and elemental carbon (EC) ambient concentrations were measured from February 2012 to January 2013 at a suburban site in the city of Guangzhou, China. The major primary OC and EC sources at this site are vehicular exhaust and regional combustion sources. The annual average OC and EC concentrations (±1SD) were 7.30 ± 5.68 and 2.67 ± 2.28 μgC/m3, respectively. Taking advantage of this large data set of more than 7,000 measurements, we examined the diurnal variation dynamics of the primary OC‐to‐EC ratio ([OC/EC]pri), derived using the minimum R squared (MRS) method, which deploys an algorithm that calculates (OC/EC)pri by exploring the assumed independency between EC and secondary OC (SOC). The MRS‐derived hour‐by‐hour (OC/EC)pri was the lowest (1.8–2.0) for the morning hours while higher (2.4–3.0) for the afternoon and evening hours. Cumulative evidence from other pollutant measurements suggests that the increase in MRS‐derived (OC/EC)pri in the afternoon and evening was a result of SOC formation from photooxidation of precursors that covary with EC (e.g., vehicular exhaust) and thereby inflate (OC/EC)pri. We subsequently separate SOC into two categories based on their correlations with EC, that is, SOCa that covary with EC and SOCb that is unassociated with EC variations. SOCa and SOCb make comparable contributions to SOC, demonstrating that their separation is essential for a more accurate SOC estimation. Otherwise, SOCa would be mistakenly accounted as primary OC. This approach in estimating SOC may underestimate SOCa and subject to bias stemming from OC emissions from cooking and biomass burning.

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Section: Resultssupporting

confidence: 83%

Estimation and Uncertainty Analysis of Secondary Organic Carbon Using 1 Year of Hourly Organic and Elemental Carbon Data

Wang

2019

JGR Atmospheres

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“…To determine the average TEs status for the whole year, several extremely high concentration values during the spring festival and spring dust storm, with Ca concentrations above 6,000 ng/m 3 , were excluded in the following analysis; the details on this can be found in the supporting information (Table S2). Based on the daily PM 2.5 concentrations, the following air quality levels were defined: excellent (E), 0 < PM 2.5 ≤ 35 μg/m 3 ; good (G), 35 < PM 2.5 ≤ 75 μg/m 3 ; slightly polluted (SP): 75 < PM 2.5 ≤ 115 μg/m 3 ; moderately polluted (MP), 115 < PM 2.5 ≤ 150 μg/m 3 ; heavily polluted (HP), PM 2.5 > 150 μg/m 3 (Ji et al, ). The variation in the concentrations of the TEs for the different air quality pollution levels is presented in Figure .…”

Section: Resultsmentioning

confidence: 99%

Characteristics and Sources of Hourly Trace Elements in Airborne Fine Particles in Urban Beijing, China

Cui

Chen

et al. 2019

JGR Atmospheres

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To better investigate the characteristics and sources of trace elements (TEs) in PM2.5 in urban Beijing, a 1‐year hourly observation was continuously made using an online multi‐element analyzer from 1 June 2016 to 31 May 2017. The average concentrations of 14 individual TEs ranged from 1.1 (V) to 900 ng/m3 (K). The occurrence levels of most TEs of interest in Beijing were lower than those in most domestic cities, but higher than those in most foreign countries. The formation of sulfate increased with the concentrations of all studied TEs during autumn and winter. Dust, industry, biomass burning and waste incineration, vehicle emissions, coal combustion, and oil combustion were identified by the positive matrix factorization (PMF) model, which accounted for 36.3%, 10.7%, 27.1%, 13.7%, 7.6%, and 4.6%, respectively, of the total elements. All factors exhibited higher concentrations on weekends than on weekdays. Local vehicular emissions and industry contributed to the loading of TEs, but dust, biomass burning and waste incineration, coal combustion, and oil combustion from neighboring areas appeared to be dominant sources of TEs. Except for dust and industry, the four other sources of TEs were mainly located in the south and southeast areas of the sampling site. The analysis by conditional probability function and potential source contribution function showed that the distribution of the PMF sources of TEs roughly agreed with the location of the main point sources. Overall, this work provides more detailed information on the characteristics of the TEs for the scientific community and modelling work.

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“…The effects of temperature on PM concentrations are complicated. High temperature in summer promotes the formation of particulate sulfate, but dissociates part of particulate nitrate [40,41], while low temperature in winter is favorable for the absorption and condensation of secondary organic aerosols [42]. Generally, temperature is not significantly correlated to PM concentrations [43,44].…”

Section: Effects Of Meteorological Parameters On Pm 25 and Pm 25-10mentioning

confidence: 95%

Potential Sources and Formations of the PM2.5 Pollution in Urban Hangzhou

Wang

et al. 2016

Atmosphere

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Continuous measurements of meteorological parameters, gaseous pollutants, particulate matters, and the major chemical species in PM 2.5 were conducted in urban Hangzhou from 1 September to 30 November 2013 to study the potential sources and formations of PM 2.5 pollution. The average PM 2.5 concentration was 69 µg¨m´3,~97% higher than the annual concentration limit in the national ambient air quality standards (NAAQS) of China. Relative humidity (RH) and wind speed (WS) were two important factors responsible for the increase of PM 2.5 concentration, with the highest value observed under RH of 70%-90%. PM 2.5 was in good correlation with both NO 2 and CO, but not with SO 2 , and the potential source contribution function (PSCF) results displayed that local emissions were important potential sources contributing to the elevated PM 2.5 and NO 2 in Hangzhou. Thus, local vehicle emission was suggested as a major contribution to the PM 2.5 pollution. Concentrations of NO 2 and CO significantly increased in pollution episodes, while the SO 2 concentration even decreased, implying local emission rather than region transport was the major source contributing to the formation of pollution episodes. The sum of SO 4 2´, NO 3´, and NH 4 + accounted for~50% of PM 2.5 in mass in pollution episodes and the NO 3´/ EC ratios were significantly elevated, revealing that the formation of secondary inorganic species, particularly NO 3´, was an important contributor to the PM 2.5 pollution in Hangzhou. This study highlights that controlling local pollution emissions was essential to reduce the PM 2.5 pollution in Hangzhou, and the control of vehicle emission in particular should be further promoted in the future.

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Characteristics of atmospheric organic and elemental carbon aerosols in urban Beijing, China

Cited by 114 publications

References 60 publications

Estimation and Uncertainty Analysis of Secondary Organic Carbon Using 1 Year of Hourly Organic and Elemental Carbon Data

Estimation and Uncertainty Analysis of Secondary Organic Carbon Using 1 Year of Hourly Organic and Elemental Carbon Data

Characteristics and Sources of Hourly Trace Elements in Airborne Fine Particles in Urban Beijing, China

Potential Sources and Formations of the PM2.5 Pollution in Urban Hangzhou

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