Amine-Enhanced Methanesulfonic Acid-Driven Nucleation: Predictive Model and Cluster Formation Mechanism

Sulfuric Acid-Driven Nucleation Enhanced by Amines from Ethanol Gasoline Vehicle Emission: Machine Learning Model and Mechanistic Study

The sulfuric acid (SA)-amine nucleation mechanism gained increasing attention due to its important role in atmospheric secondary particle formation. However, the intrinsic enhancing potential (IEP) of various amines remains largely unknown, restraining the assessment on the role of the SA-amines mechanism at various locations. Herein, machine learning (ML) models were constructed for high-throughput prediction of IEP of amines, and the nucleation mechanism of specific amines with high IEP was investigated. The formation free energy (ΔG) of SA-amines dimer clusters, a key parameter for assessing IEP, was calculated for 58 amines. Based on the calculated ΔG values, seven ML models were constructed and the best one was further utilized to predict the ΔG values of the remaining 153 amines. Diethylamine (DEA), mainly emitted from ethanol gasoline vehicles, was found to be one of the amines with the highest IEP for SA-driven nucleation. By studying larger SA-DEA clusters, it was found that the nucleation rate of DEA with SA is 3-7 times higher than that of dimethylamine, a well-known key base for SA-driven nucleation. The study provides a powerful tool for evaluating the actual role of amines on SA-driven nucleation and revealed that the mechanism could be particularly important in areas where ethanol gasoline vehicles are widely used.

Implications for new particle formation in air of the use of monoethanolamine in carbon capture and storage

Alkanolamines are currently being deployed in carbon capture and storage (CCS) technology worldwide, and atmospheric emissions have been found to coincide with locations exhibiting elevated concentrations of methanesulfonic acid (MSA). It is thus critical to understand the fate and potential atmospheric reactions of these chemicals. This study reports the characterization of sub-10 nm nanoparticles produced through the acid-base reaction between gas phase monoethanolamine (MEA) and MSA, a product of organosulfur compound oxidation in air, using a flow reactor under dry and humid (up to ∼60% RH) conditions. Number size distribution measurements show that MEA is even more efficient than methylamine in forming nanoparticles on reaction with MSA. This is attributed to the fact that the MEA structure contains both an -NH2 and an -OH group that facilitate hydrogen bonding within the clusters, in addition to the electrostatic interactions. Due to this already strong H-bond network, water has a relatively small influence on new particle formation (NPF) and growth in this system, in contrast to MSA reactions with alkylamines. Acid/base molar ratios of unity for 4-12 nm particles were measured using thermal desorption chemical ionization mass spectrometry. The data indicate that reaction of MEA with MSA may dominate NPF under some atmospheric conditions. Thus, the unique characteristics of alkanolamines in NPF must be taken into account for accurate predictions of impacts of CCS on visibility, health and climate.

HIO3-HIO2-Driven Three-Component Nucleation: Screening Model and Cluster Formation Mechanism

Iodine oxoacids (HIO3 and HIO2)-driven nucleation has been suggested to efficiently contribute to new particle formation (NPF) in marine atmospheres. Abundant atmospheric nucleation precursors may further enhance HIO3-HIO2-driven nucleation through various multicomponent nucleation mechanisms. However, the specific enhancing potential (EP) of different precursors remains largely unknown. Herein, the EP-based screening model of precursors and enhancing mechanism of the precursor with the highest EP on HIO3-HIO2 nucleation were investigated. The formation free energies (ΔG), as critical parameters for evaluating EP, were calculated for the dimers of 63 selected precursors with HIO2. Based on the ΔG values, (1) a quantitative structure-activity relationship model was developed for evaluating ΔG of other precursors and (2) atmospheric concentrations of 63 (precursor)1(HIO2)1 dimer clusters were assessed to identify the precursors with the highest EP for HIO3-HIO2-driven nucleation by combining with earlier results for the nucleation with HIO3 as the partner. Methanesulfonic acid (MSA) was found to be one of the precursors with the highest EP. Finally, we found that MSA can effectively enhance HIO3-HIO2 nucleation at atmospheric conditions by studying larger MSA-HIO3-HIO2 clusters. These results augment our current understanding of HIO3-HIO2 and MSA-driven nucleation and may suggest a larger impact of HIO2 in atmospheric aerosol nucleation.

The application of machine learning to air pollution research: A bibliometric analysis

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.

Enhancement of Atmospheric Nucleation Precursors on Iodic Acid-Induced Nucleation: Predictive Model and Mechanism

Iodic acid (IA) has recently been recognized as a key driver for new particle formation (NPF) in marine atmospheres. However, the knowledge of which atmospheric vapors can enhance IA-induced NPF remains limited. The unique halogen bond (XB)-forming capacity of IA makes it difficult to evaluate the enhancing potential (EP) of target compounds on IA-induced NPF based on widely studied sulfuric acid systems. Herein, we employed a three-step procedure to evaluate the EP of potential atmospheric nucleation precursors on IA-induced NPF. First, we evaluated the EP of 63 precursors by simulating the formation free energies (ΔG) of the IA-containing dimer clusters. Among all dimer clusters, 44 contained XBs, demonstrating that XBs are frequently formed. Based on the calculated ΔG values, a quantitative structure-activity relationship model was developed for evaluating the EP of other precursors. Second, amines and O/S-atom-containing acids were found to have high EP, with diethylamine (DEA) yielding the highest potential to enhance IA-induced nucleation by combining both the calculated ΔG and atmospheric concentration of considered 63 precursors. Finally, by studying larger (IA)_1-3(DEA)_1-3 clusters, we found that the IA-DEA system with merely 0.1 ppt (2.5×10⁶ cm^-3) DEA yields comparable nucleation rates to that of the IA-iodous acid system.

The role of hydration in atmospheric salt particle formation

New-particle formation from condensable acid and base molecules is a ubiquitous phenomenon in the atmosphere. The role of water in salt particle formation is not fully understood as it can stabilize or destabilize cluster structures, which leads to non-linear effects on cluster formation dynamics. In the studied systems, increased relative humidity can enhance the particle formation for up to four orders of magnitude in the case of nitric acid, but it can also slightly reduce the particle formation in the cases of sulfuric acid and methanesulfonic acid. As the effect of relative humidity in salt particle formation varies many orders of magnitude depending on the acid and base molecules, neglecting hydration or using the same value for different systems may introduce remarkable inaccuracies in large-scale models.

The driving effects of common atmospheric molecules for formation of prenucleation clusters: the case of sulfuric acid, formic acid, nitric acid, ammonia, and dimethyl amine

How secondary aerosols form is critical as aerosols' impact on Earth's climate is one of the main sources of uncertainty for understanding global warming. The beginning stages for formation of prenucleation complexes, that lead to larger aerosols, are difficult to decipher experimentally. We present a computational chemistry study of the interactions between three different acid molecules and two different bases. By combining a comprehensive search routine covering many thousands of configurations at the semiempirical level with high level quantum chemical calculations of approximately 1000 clusters for every possible combination of clusters containing a sulfuric acid molecule, a formic acid molecule, a nitric acid molecule, an ammonia molecule, a dimethylamine molecule, and 0-5 water molecules, we have completed an exhaustive search of the DLPNO-CCSD(T)/CBS//ωB97X-D/6-31++G** Gibbs free energy surface for this system. We find that the detailed geometries of each minimum free energy cluster are often more important than traditional acid or base strength. Addition of a water molecule to a dry cluster can enhance stabilization, and we find that the (SA)(NA)(A)(DMA)(W) cluster has special stability. Equilibrium calculations of SA, FA, NA, A, DMA, and water using our quantum chemical ΔG° values for cluster formation and realistic estimates of the concentrations of these monomers in the atmosphere reveals that nitric acid can drive early stages of particle formation just as efficiently as sulfuric acid. Our results lead us to believe that particle formation in the atmosphere results from the combination of many different molecules that are able to form highly stable complexes with acid molecules such as SA, NA, and FA.

Contribution of Methanesulfonic Acid to the Formation of Molecular Clusters in the Marine Atmosphere

Because of the lack of long-term measurements, new particle formation (NPF) in the marine atmosphere remains puzzling. Using quantum chemical methods, this study elucidates the cluster formation and further growth of sulfuric acid-methanesulfonic acid-dimethylamine (SA-MSA-DMA) clusters, relevant to NPF in the marine atmosphere. The cluster structures and thermochemical parameters of (SA)_n(MSA)_m(DMA)_l (n + m ≤ 4 and l ≤ 4) systems are calculated using density functional theory at the ωB97X-D/6-31++G(d,p) level of theory, and the single-point energies are calculated using high-level DLPNO-CCSD(T₀)/aug-cc-pVTZ calculations. The calculated thermochemistry is used as input to the Atmospheric Cluster Dynamics Code (ACDC) to gain insight into the cluster dynamics. At ambient conditions (298.15 K, 1 atm), we find that the distribution of outgrowing clusters primarily consists of SA and DMA, with a minor contribution from the mixed SA-MSA-DMA clusters. At lower temperature (278.15 K, 1 atm) the distribution broadens, and clusters containing one or more MSA molecules emerge. These findings show that in the cold marine atmosphere MSA likely participates in atmospheric NPF.