A parameterization of sulfuric acid-dimethylamine nucleation and its application in three-dimensional modeling
Abstract. Sulfuric acid (SA) is a governing gaseous precursor for atmospheric new particle formation (NPF) in diverse environments, which is a major source of global ultrafine particles. In polluted urban atmosphere with high condensation sink (CS), the formation of stable SA-amine clusters, such as SA-DMA clusters, usually initializes intense NPF events. Coagulation scavenging and cluster evaporation are dominant sink processes of SA-amine clusters in urban atmosphere, yet they are not quantitatively included in the present parameterizations of SA-amine nucleation. We herein report a parameterization of SA-DMA nucleation based on cluster dynamic simulations and quantum chemistry calculations, with certain simplifications to largely reduce the computational costs. Compared with previous SA-DMA nucleation parameterizations, this new parameterization would be able to reproduce the dependences of particle formation rates on temperature and CS. We then incorporated it in a three-dimensional chemical transport model to simulate the evolution of particle number size distributions. Simulation results show good consistency with the observations in the occurrence of NPF events and particle number size distributions in wintertime Beijing, showing a significant improvement compared to that using parameterization without coagulation scavenging. Quantitative analysis shows that SA-DMA nucleation contributes majorly to nucleation rates and aerosol population during the 3-D simulations in Beijing (> 99 % and > 60 %, respectively). These results broaden the understanding of NPF in urban atmospheres and stress the necessity of including the effects of coagulation scavenging and cluster stability in simulating SA-DMA nucleation in three-dimensional simulations. This would improve the performance in particle source apportionment and quantification of aerosol effects on air quality, human health, and climate.
Yuyang Li et al.
Status: open (until 28 Mar 2023)
- RC1: 'Comment on acp-2023-15', Anonymous Referee #1, 19 Feb 2023 reply
Yuyang Li et al.
Yuyang Li et al.
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The study presents a parameterization of (mostly) previously published detailed modeling of sulfuric-acid- dimethylamine (DMA) new particle formation (NPF) and a modeling study in which the parameterization is applied over Beijing and found to explain aerosol number concentrations there. The parameterization of the detailed models and the method of simulating DMA concentrations are interesting, and useful. The paper could be improved by making the statements in the text more quantitative. I recommend that the paper is within scope of ACP and will be suitable for publication if the following mainly minor comments can be addressed.
1. Need to evaluate condensation sink (CS) timeseries in WRF-chem simulations to show that simulated 1nm aerosol concentrations aren’t right for the wrong reasons. Please discuss results with reference to how sensitive the NPF rates are to the CS (Figure 2). While some information might be gained from the banana plots, they are hard to interpret quantitatively, and the size distribution is averaged over the time-period, which might hide significant discrepancies and might also help explain issue in simulating 2-10nm-sized aerosols.
2. Authors need to follow ACP open data standards: https://www.atmospheric-chemistry-and-physics.net/policies/data_policy.html. It is no longer sufficient to say "data are available from the authors on request'. Post simulation output and all code and data needed to reproduce figures on a repository, at a minimum.
The introduction refers to much of the relevant literature on NPF parameterizations but does not review prior modeling work in Beijing. Also need to discuss complementary modeling of SA-DMA clustering by Liu et al 2021, https://www.pnas.org/doi/epdf/10.1073/pnas.2108384118
Is there any dependence of the SA-DMA NPF rates on relative humidity?
The evaporation rate of 3.33s-1 is different to the one in Kuerten et al (2018), 0.1s-1 and likely other studies. Why?
What about synergistic effects involving ammonia or nitric acid (Glasoe et al, 2015; Liu et al 2021) and what about the possible role of amines other than DMA or even malic acid (Liu et al, Phys Chem Chem Phys, 2022 10.1039/D2CP03551K).
Sections 3.1 and 3.2 are too brief and qualitative: need to quantify the biases in the parameterized J rates versus the KM and CDS models using normalized mean bias and R^2.
Section 3.3: what are the R^2 values between the measured and simulated SA and DMA concentrations? Daytime SA is usually simulated within a factor 2 of measurements, which looks good, and the measurement uncertainty is likely close to a factor of 2 if not higher, so you can’t be expected to do much better than this – but a factor of 2 in SA is a factor of 16 in SA-DMA nucleation if the power law is 4. Doesn’t this introduce an important uncertainty in the simulated aerosol number concentration?
Section 3.4: In Figure 6, it looks like only about half of the NPF events simulated actually happened. It would be good to put some more precise numbers on this in the text. And in Figure 4, there seem to be too many Aitken mode aerosols most of the time. Doesn’t this suggest the SA-DMA NPF mechanism is too strong? What could cause these biases?
How frequently were relevant diagnostic variables output from WRF-chem?
Figure 1: the dashed line represents a factor of four variation in a) and an order of magnitude in b). There is no grey dashed line for +/-50% variation.
Line 308/Figure S6: Dunne et al (2016) included SA-DMA nucleation in their model but did not present it as part of their main analysis. Was only SA-H2O and SA-NH3-H2O nucleation from CLOUD included in this comparison, or were all mechanisms included? Please clarify.
All the Supplementary figures should be explicitly referred to in the text, or removed – but it may be enough to change S9-S16 to S9-S18 on line 362.
Would be good to try to link sensitivity studies more closely to observed biases in the results – discuss how uncertainties in X could lead to biases in Y etc.
Numerous small grammatical mistakes, e.g. missing “the”, use of “largely” to mean “greatly”, new words such as “majorly”, consistency between singular and plural verb forms (e.g 'the clusters… is…') throughout should be fixed before publication.