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Fine-particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models
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Fine-particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models
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Journal Article
Fine-particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models
2018
Overview
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.
