Insufficient Condensable Organic Vapors Lead to Slow Growth of New Particles in an Urban Environment

Advances in analysis of atmospheric ultrafine particles and application in air quality, climate, and health research

There is growing interest in the contribution of ultrafine particles to air quality, climate, and human health. Ultrafine particles are of central significance for the influence of radiative forcing of climate change by involving in the formation of clouds and precipitation. Moreover, exposure to ultrafine particles can enhance the disease burden. The determination of those effects of ultrafine particles strongly depends on their chemical composition and physicochemical properties. This review focuses on the advanced techniques for the characterization of chemical composition and physicochemical properties of ultrafine particles in the past five years. The current analytical methodologies are broadly classified into electron and X-ray microscopy, optical spectroscopy and microscopy, electrical mobility, and mass spectrometry, and then described and discussed its operation principle, advantages, and limitations. Besides measurements, application of the state-of-the-art techniques is briefly reviewed to help us to promote a better understanding of atmospheric ultrafine particles relevant to air quality, climate, and health.

Resolving Atmospheric Oxygenated Organic Molecules in Urban Beijing Using Online Ultrahigh-Resolution Chemical Ionization Mass Spectrometry

Gaseous oxygenated organic molecules (OOMs) are crucial precursors of atmospheric organic aerosols. OOMs in urban atmospheres have complex compositions, posing challenges to understanding their formation, evolution, and influences. In this study, we identify 2403 atmospheric gaseous OOMs in urban Beijing using online nitrate-based chemical ionization Orbitrap mass spectrometry based on one-year atmospheric measurements. We find that OOMs in urban atmospheres can be identified with higher accuracy and wider coverage, compared to previously used online mass spectrometry. With optimized OOM resolving capabilities, previous knowledge of OOMs in urban atmospheres can be expanded. First, clear homologous and oxygen-addition characteristics of the OOMs are revealed. Second, OOMs with lower concentrations or higher masses are identified and characterized with high confidence, e.g., OOMs with masses above 350 Da. In particular, dimers of OOMs (e.g., C20H32O8-15N2), crucial species for organic nucleation, are identified. During four seasons, nitrogen-containing OOMs dominate the total concentration of OOMs, and OOMs are mainly from aromatic and aliphatic oxidation. Additionally, radicals with similar composition as OOMs, intermediates for OOM formation, are identified with clear diurnal variation, e.g., CnH2n-5O6 radicals (n = 8-10) and CmH2m-4NO9 radicals (m = 9-10), peak during the daytime and nighttime, respectively, previously having scarce measurement evidence in urban atmospheres.

Road Traffic Emissions Lead to Much Enhanced New Particle Formation through Increased Growth Rates

New particle formation (NPF) is a major source of atmospheric aerosol particles, including cloud condensation nuclei (CCN), by number globally. Previous research has highlighted that NPF is less frequent but more intense at roadsides compared to urban background. Here, we closely examine NPF at both background and roadside sites in urban Central Europe. We show that the concentration of oxygenated organic molecules (OOMs) is greater at the roadside, and the condensation of OOMs along with sulfuric acid onto new particles is sufficient to explain the growth at both sites. We identify a hitherto unreported traffic-related OOM source contributing 29% and 16% to total OOMs at the roadside and background, respectively. Critically, this hitherto undiscovered OOM source is an essential component of urban NPF. Without their contribution to growth rates and the subsequent enhancements to particle survival, the number of >50 nm particles produced by NPF would be reduced by a factor of 21 at the roadside site. Reductions to hydrocarbon emissions from road traffic may thereby reduce particle numbers and CCN counts.

Fostering a Holistic Understanding of the Full Volatility Spectrum of Organic Compounds from Benzene Series Precursors through Mechanistic Modeling

A comprehensive understanding of the full volatility spectrum of organic oxidation products from the benzene series precursors is important to quantify the air quality and climate effects of secondary organic aerosol (SOA) and new particle formation (NPF). However, current models fail to capture the full volatility spectrum due to the absence of important reaction pathways. Here, we develop a novel unified model framework, the integrated two-dimensional volatility basis set (I2D-VBS), to simulate the full volatility spectrum of products from benzene series precursors by simultaneously representing first-generational oxidation, multigenerational aging, autoxidation, dimerization, nitrate formation, etc. The model successfully reproduces the volatility and O/C distributions of oxygenated organic molecules (OOMs) as well as the concentrations and the O/C of SOA over wide-ranging experimental conditions. In typical urban environments, autoxidation and multigenerational oxidation are the two main pathways for the formation of OOMs and SOA with similar contributions, but autoxidation contributes more to low-volatility products. NOx can reduce about two-thirds of OOMs and SOA, and most of the extremely low-volatility products compared to clean conditions, by suppressing dimerization and autoxidation. The I2D-VBS facilitates a holistic understanding of full volatility product formation, which helps fill the large gap in the predictions of organic NPF, particle growth, and SOA formation.

Effects of volatile organic compounds and new particle formation on real-time hygroscopicity of PM2.5 particles in Seosan, Republic of Korea

The hygroscopicity of PM2.5 particles plays an important role in PM2.5 haze in Northeast Asian countries by influencing particle growth and chemical composition. New particle formation (NPF) and atmospheric volatile organic compounds (VOCs) are factors that influence particle hygroscopicity. However, the lack of real-time hygroscopicity measurements has deterred the understanding of their effects on particle hygroscopicity. In this study, two intensive monitoring campaigns were conducted during the summer of 2021 and spring of 2022 using real-time aerosol instruments, including a humidified tandem differential mobility analyzer (HTDMA), in Seosan, Republic of Korea. The hygroscopicity parameter κ was calculated from the real-time HTDMA measurement data (κGf). The diurnal variations in κGf exhibited strong inverse linear correlations with the total concentration of VOCs (CTVOC) during the two campaigns. The higher atmospheric CTVOC in summer increased the growth rate of the particle diameter from 10 to 40 nm (6 nm/h) compared with that in spring (2.7 nm/h), resulting in a faster change in κGf for 40-nm particles in summer than in spring because of the increase in organic matter in the chemical compositions of particles. In addition, NPF events introduced additional tiny fresh particles into the atmosphere, which reduced the κGf of 40-nm particles and increased the intensity of the less hygroscopic peaks (κGf < 0.1) of κ-probability density functions (κ-PDF) in NPF days. However, 100-nm particles exhibited fewer changes in κGf than 40-nm particles, resulting in additional dominant hygroscopic peaks (κ ∼ 0.2) of κ-PDFs in both NPF and non-NPF days. When κGf values measured in Seosan were compared with those in other Northeast Asian countries in the literature, the κ values for 40-nm particles were lower than those (κ > 0.2) measured in Beijing and Guangzhou, but those for 100-nm particles were close to those measured in the two cities.

Development of an automatic measurement system using atmospheric pressure photoionization ultrahigh-resolution mass spectrometry and application for on-line analysis of particulate matter

On-line chemical characterization of atmospheric particulate matter (PM) with soft ionization technique and ultrahigh-resolution Mass Spectrometry (UHRMS) provides molecular information of organic constituents in real time. Here we describe the development and application of an automatic measurement system that incorporates PM2.5 sampling, thermal desorption, atmospheric pressure photoionization, and UHRMS analysis. Molecular formulas of detected organic compounds were deducted from the accurate (±10 ppm) molecular weights obtained at a mass resolution of 100,000, allowing the identification of small organic compounds in PM2.5. Detection efficiencies of 28 standard compounds were determined and we found a high sensitivity and selectivity towards organic amines with limits of detection below 10 pg. As a proof of principle, PM2.5 samples collected off-line in winter in the urban area of Beijing were analyzed using the Ionization Module and HRMS of the system. The automatic system was then applied to conduct on-line measurements during the summer time at a time resolution of 2 hr. The detected organic compounds comprised mainly CHON and CHN compounds below 350 m/z. Pronounced seasonal variations in elemental composition were observed with shorter carbon backbones and higher O/C ratios in summer than that in winter. This result is consistent with stronger photochemical reactions and thus a higher oxidation state of organics in summer. Diurnal variation in signal intensity of each formula provides crucial information to reveal its source and formation pathway. In summary, the automatic measurement system serves as an important tool for the on-line characterization and identification of organic species in PM2.5.

The Significant Role of New Particle Composition and Morphology on the HNO3-Driven Growth of Particles down to Sub-10 nm

New particle formation and growth greatly influence air quality and the global climate. Recent CERN Cosmics Leaving OUtdoor Droplets (CLOUD) chamber experiments proposed that in cold urban atmospheres with highly supersaturated HNO3 and NH3, newly formed sub-10 nm nanoparticles can grow rapidly (up to 1000 nm h-1). Here, we present direct observational evidence that in winter Beijing with persistent highly supersaturated HNO3 and NH3, nitrate contributed less than ∼14% of the 8-40 nm nanoparticle composition, and overall growth rates were only ∼0.8-5 nm h-1. To explain the observed growth rates and particulate nitrate fraction, the effective mass accommodation coefficient of HNO3 (αHNO3) on the nanoparticles in urban Beijing needs to be 2-4 orders of magnitude lower than those in the CLOUD chamber. We propose that the inefficient uptake of HNO3 on nanoparticles is mainly due to the much higher particulate organic fraction and lower relative humidity in urban Beijing. To quantitatively reproduce the observed growth, we show that an inhomogeneous "inorganic core-organic shell" nanoparticle morphology might exist for nanoparticles in Beijing. This study emphasized that growth for nanoparticles down to sub-10 nm was largely influenced by their composition, which was previously ignored and should be considered in future studies on nanoparticle growth.

New mechanism for the participation of aromatic oxidation products in atmospheric nucleation

Oxygenated organic molecules (OOMs) are recognized as important precursors for new particle formation (NPF) in the urban atmosphere. The paper theoretically studied the formation of OOMs by styrene oxidation processes initiated by OH radicals, focusing on the OOMs nucleation mechanism. The results found that in the presence of an H2SO4 molecule, lowly oxygenated organic molecules containing a benzene ring (LOMBs) can form stable clusters and grow to the scale of a critical nucleus through pi-pi stacking and OH hydrogen bonding. In addition, LOMBs are more readily generated in a styrene-oxidized system in the presence/absence of NOx than highly oxygenated organic molecules (HOMs). The reaction of OH radicals with other aromatics containing a branched chain on the benzene ring produces LOMBs to varying degrees, with pi-pi stacking playing an essential role. This result suggests that, in the presence of H2SO4 molecules, LOMBs may play a more significant role in promoting nucleation than HOMs. Our findings serve as a pivotal foundation for future investigations into the oxidation and nucleation processes of diverse aromatics in urban environments.

New Insights on the Formation of Nucleation Mode Particles in a Coastal City Based on a Machine Learning Approach

Atmospheric particles have profound implications for the global climate and human health. Among them, ultrafine particles dominate in terms of the number concentration and exhibit enhanced toxic effects as a result of their large total surface area. Therefore, understanding the driving factors behind ultrafine particle behavior is crucial. Machine learning (ML) provides a promising approach for handling complex relationships. In this study, three ML models were constructed on the basis of field observations to simulate the particle number concentration of nucleation mode (PNCN). All three models exhibited robust PNCN reproduction (R2 > 0.80), with the random forest (RF) model excelling on the test data (R2 = 0.89). Multiple methods of feature importance analysis revealed that ultraviolet (UV), H2SO4, low-volatility oxygenated organic molecules (LOOMs), temperature, and O3 were the primary factors influencing PNCN. Bivariate partial dependency plots (PDPs) indicated that during nighttime and overcast conditions, the presence of H2SO4 and LOOMs may play a crucial role in influencing PNCN. Additionally, integrating additional detailed information related to emissions or meteorology would further enhance the model performance. This pilot study shows that ML can be a novel approach for simulating atmospheric pollutants and contributes to a better understanding of the formation and growth mechanisms of nucleation mode particles.

Modeling the Formation of Organic Compounds across Full Volatility Ranges and Their Contribution to Nanoparticle Growth in a Polluted Atmosphere

Nanoparticle growth influences atmospheric particles' climatic effects, and it is largely driven by low-volatility organic vapors. However, the magnitude and mechanism of organics' contribution to nanoparticle growth in polluted environments remain unclear because current observations and models cannot capture organics across full volatility ranges or track their formation chemistry. Here, we develop a mechanistic model that characterizes the full volatility spectrum of organic vapors and their contributions to nanoparticle growth by coupling advanced organic oxidation modeling and kinetic gas-particle partitioning. The model is applied to Nanjing, a typical polluted city, and it effectively captures the volatility distribution of low-volatility organics (with saturation vapor concentrations <0.3 μg/m3), thus accurately reproducing growth rates (GRs), with a 4.91% normalized mean bias. Simulations indicate that as particles grow from 4 to 40 nm, the relative fractions of GRs attributable to organics increase from 59 to 86%, with the remaining contribution from H2SO4 and its clusters. Aromatics contribute much to condensable organic vapors (∼37%), especially low-volatility vapors (∼61%), thus contributing the most to GRs (32-46%) as 4-40 nm particles grow. Alkanes also contribute 19-35% of GRs, while biogenic volatile organic compounds contribute minimally (<13%). Our model helps assess the climatic impacts of particles and predict future changes.

High contribution of new particle formation to ultrafine particles in four seasons in an urban atmosphere in south China

Ultra fine particles (UFP) cover the size range of both nucleation mode particles (NUC, Dp < 25 nm) and Aitken mode particles (AIT, 25 nm < Dp < 100 nm), and play important roles in radiative forcing and human health. In this study, we identified new particle formation (NPF) events and undefined events, explored their potential formation mechanism, and quantified their contributions to UFP number concentration (NUFP) in urban Dongguan of the Pearl River Delta (PRD) region. Field campaigns were carried out in four seasons in 2019 to measure particle number concentration in the size range of 4.7-673.2 nm, volatile organic compounds (VOCs), gaseous pollutants, chemical compositions in PM2.5, and meteorological parameters. The frequency of the occurrence of NPF, as indicated by a significant increase in NUC number concentration (NNUC), was 26 %, and that of the undefined event, as indicated by substantial increases in NNUC or AIT number concentration (NAIT), was 32 % during the whole campaign period. The NPF events mainly occurred in autumn (with a frequency of 59 %) and winter (33 %) and only occasionally in spring (4 %) and summer (4 %). On the contrary, the frequencies of the undefined events were higher in spring (52 %) and summer (38 %) than in autumn (19 %) and winter (22 %). The burst periods of the NPF events mainly occurred before 11:00 Local Time (LT), while those of the undefined events mainly occurred after 11:00 LT. Accompanied to NPF events were low concentrations of VOCs and high concentrations of O3. The undefined events by NUC or AIT were associated with the upwind transport of newly formed particles. Source apportionment analysis suggested that NPF and undefined events were the largest contributor to NNUC (51 ± 28 %), NAIT (41 ± 26 %), and NUFP (45 ± 27 %), while coal combustion and biomass burning, and traffic emission were the second largest contributor to NNUC (22 ± 20 %) and NAIT (39 ± 28 %), respectively.

Does income inequality reshape the environmental Kuznets curve (EKC) hypothesis? A nonlinear panel data analysis

The COVID-19 pandemic has further increased income inequality. This work is aimed to explore the impact of income inequality on the environmental Kuznets curve (EKC) hypothesis. To this end, income inequality is set as the threshold variable, economic growth is set as the explanatory variable, while carbon emission is set as the explained variable, and the threshold panel model is developed using the data of 56 countries. The empirical results show that income inequality has changed the relationship between economic growth and carbon emissions from an inverted U-shaped to an N-shaped, which means that income inequality redefines the environmental Kuznets curve and increases the complexity of the decoupling of economic growth and carbon emissions. Specifically, economic growth significantly increases carbon emissions during periods of low income inequality, however, as income inequality increases, economic growth in turn suppresses carbon emissions. In the period of high income inequality, economic growth inhibits the increase of carbon emissions. However, with the increase of income inequality, the impact of economic growth on carbon emission changes from inhibiting to promoting. Panel regressions for robustness tests show that this phenomenon is more pronounced in high-income countries. We therefore contend that the excessive income inequality is bad for the win-win goal of economic growth without carbon emission growth, and the income distribution policy should be included in the carbon neutral strategy.