Variations of Chemical Composition and Source Apportionment of PM2.5 during Winter Haze Episodes in Beijing

Ma, Qingxia; Wu, Yunfei; Tao, Jun; Xia, Yun‐Jie; Liu, Xinyu; Zhang, Daizhou; Han, Zhiwei; Zhang, Renjian

doi:10.4209/aaqr.2017.10.0366

Cited by 35 publications

(27 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It affects the solubility of trace metals and thus aerosol toxicity (Ghio et al, 2012;. Low pH may enhance iron mobility in dust and impact ocean productivity (Meskhidze et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Fine-particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models

et al. 2018

View full text Add to dashboard Cite

pH is an important property of aerosol particles but is difficult to measure directly. Several studies have estimated the pH values for fine particles in northern China winter haze using thermodynamic models (i.e., E-AIM and ISORROPIA) and ambient measurements. The reported pH values differ widely, ranging from close to 0 (highly acidic) to as high as 7 (neutral). In order to understand the reason for this discrepancy, we calculated pH values using these models with different assumptions with regard to model inputs and particle phase states. We find that the large discrepancy is due primarily to differences in the model assumptions adopted in previous studies. Calculations using only aerosol-phase composition as inputs (i.e., reverse mode) are sensitive to the measurement errors of ionic species, and inferred pH values exhibit a bimodal distribution, with peaks between −2 and 2 and between 7 and 10, depending on whether anions or cations are in excess. Calculations using total (gas plus aerosol phase) measurements as inputs (i.e., forward mode) are affected much less by these measurement errors. In future studies, the reverse mode should be avoided whereas the forward mode should be used. Forward-mode calculations in this and previous studies collectively indicate a moderately acidic condition (pH from about 4 to about 5) for fine particles in northern China winter haze, indicating further that ammonia plays an important role in determining this property. The assumed particle phase state, either stable (solid plus liquid) or metastable (only liquid), does not significantly impact pH predictions. The unrealistic pH values of about 7 in a few previous studies (using the standard ISORROPIA model and stable state assumption) resulted from coding errors in the model, which have been identified and fixed in this study.

show abstract

“…It affects the solubility of trace metals and thus aerosol toxicity (Ghio et al, 2012;. Low pH may enhance iron mobility in dust and impact ocean productivity (Meskhidze et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Fine-particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models

et al. 2018

View full text Add to dashboard Cite

show abstract

“…In the past 2 decades, the morphologies of BC aggregates have also been widely studied using tandem mobility techniques (Park et al, 2008). Measurements obtained using an impactor (e.g., an electrical low-pressure impactor, ELPI) or a particle mass analyzer (e.g., an aerosol particle mass analyzer, APM, or a centrifugal particle mass analyzer, CPMA) connected in tandem with a differential mobility analyzer (DMA) have revealed the relationship between particle mass and mobility (Park et al, 2003;Maricq and Xu, 2004;Olfert et al, 2007;Rissler et al, 2014;Sorensen, 2011; and associated references therein). The derived mass-mobility scaling exponents (D fm ), which have also been called fractal dimensions in some of these references, varied over a wide range of 2.2-2.8 for diesel exhaust particles.…”

Section: Introductionmentioning

confidence: 99%

A study of the morphology and effective density of externally mixed black carbon aerosols in ambient air using a size-resolved single-particle soot photometer (SP2)

Xia

Huang

et al. 2019

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

Abstract. The morphology and effective density of externally mixed black carbon (extBC) aerosols, important factors affecting the radiative forcing of black carbon, were studied using a tandem technique coupling a differential mobility analyzer (DMA) with a single-particle soot photometer (SP2). The study extended the mass–mobility relationship to large extBC particles with a mobility diameter (dmob) larger than 350 nm, a size range seldom included in previous tandem measurements of BC aggregates in the atmosphere. The experiment was conducted at an urban site in Beijing during a 19 d winter period from 23 January to 10 February 2018. Ambient dry particles were selected by the DMA, and the size-resolved extBC particles were distinguished from particles with a thick coating (internally mixed) according to the time delay between the incandescence signal peak and the scattering peak detected by the SP2. The masses of the extBC particles were then quantified. The time differences between the DMA size selection and the SP2 measurement were processed previously. The normalized number size distributions were investigated at the prescribed dmob sizes in the range of 140–750 nm to provide the typical mass of extBC at each dmob. On this basis, the mass–mobility relationship of the ambient extBC was established, inferring a mass–mobility scaling exponent (Dfm) (an important quantity for characterizing the morphology of fractal-like BC aggregates) with a value of 2.34±0.03 in the mobility range investigated in this study. This value is comparable with those of diesel exhaust particles, implying a predominant contribution of vehicle emissions to the ambient extBC in urban Beijing. Compared to the clean period, a higher Dfm value was observed in the polluted episode, indicating a more compact BC aggregate structure than that in the clean period. The effective densities (ρeff) of the extBC in the same dmob range were also derived, with values gradually decreasing from 0.46 g cm−3 at 140 nm mobility to 0.14 g cm−3 at 750 nm mobility. The ρeff values were slightly lower than those measured using the DMA–aerosol particle mass analyzer (APM) system. The difference in ρeff values was likely due to the lower BC masses determined by the SP2 compared to those measured by the APM at the same mobility, since the SP2 measured the refractory BC (rBC) mass instead of the total mass of the BC aggregate, which consists of both rBC and a possible fraction of nonrefractory components measured by the APM. The ρeff values in the 280–350 nm dmob range were much closer to the values for soot aggregates reported in the literature. It might be related to the more compact structure of BC aggregates in this range, resulting from the reconstruction effect by volatile and/or semivolatile components in the atmosphere. The reconstruction effect might also result in a hiatus in the increased dynamic shape factor in the range of 200–350 nm, which presented an overall increase from 2.16 to 2.93 in the 140–750 nm dmob range.

show abstract

“…Furthermore, an equivalent ratio of near unity may not indicate that fine particles of winter haze have a pH of around 7 or close to 7 Cheng et al, 2016;Ma et al, 2017). The forward mode calculations 10 (using gas + aerosol quantity as model inputs) are affected much less by the measurement errors and should be used to predict the pH for winter haze particles.…”

Section: Reverse Mode Calculationsmentioning

confidence: 99%

Fine particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models

Song¹,

Gao²,

Xu³

et al. 2018

Preprint

View full text Add to dashboard Cite

Abstract. pH is an important property of aerosol particles but is difficult to measure directly. Several studies have estimated the pH values for fine particles in North China winter haze using thermodynamic models (i.e., E-AIM and ISORROPIA) and ambient measurements. The reported pH values differ widely, ranging from close to 0 (highly acidic) to as high as 7 (neutral).In order to understand the reason for this discrepancy, we calculated pH values using these models with different assumptions 25 with regard to model inputs and particle phase states. We find that the large discrepancy is due primarily to differences in the model assumptions adopted in previous studies. Calculations using only aerosol phase composition as inputs (i.e., reverse mode) are sensitive to the measurement errors of ionic species and inferred pH values exhibit a bimodal distribution with peaks between −2 and 2 and between 7 and 10. Calculations using total (gas plus aerosol phase) measurements as inputs (i.e., forward mode) are affected much less by the measurement errors, and results are thus superior to those obtained from the reverse mode 30 calculations. Forward mode calculations in this and previous studies collectively indicate a moderately acidic condition (pH from about 4 to about 5) for fine particles in North China winter haze, indicating further that ammonia plays an important role in determining this property. The differences in pH predicted by the forward mode E-AIM and ISORROPIA calculations may be attributed mainly to differences in estimates of activity coefficients for hydrogen ions. The phase state assumed, which can be either stable (solid plus liquid) or metastable (only liquid), does not significantly impact pH predictions of ISORROPIA. 35Atmos. Chem. Phys. Discuss., https://doi

show abstract

Variations of Chemical Composition and Source Apportionment of PM2.5 during Winter Haze Episodes in Beijing

Cited by 35 publications

References 48 publications

Fine-particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models

Fine-particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models

A study of the morphology and effective density of externally mixed black carbon aerosols in ambient air using a size-resolved single-particle soot photometer (SP2)

Fine particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models

Contact Info

Product

Resources

About