Cloud condensation nuclei (CCN) are mediators of aerosol‐cloud interactions, which contribute to the largest uncertainty in climate change prediction. Here, we present a machine learning (ML)/artificial intelligence (AI) model that quantifies CCN from model‐simulated aerosol composition, atmospheric trace gas, and meteorological variables. Comprehensive multi‐campaign airborne measurements, covering varied physicochemical regimes in the troposphere, confirm the validity of and help probe the inner workings of this ML model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. ML extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust ML pathway in global climate models that only resolve aerosol composition; potentially mitigating the uncertainty of effective radiative forcing due to aerosol‐cloud interactions (ERF_aci) and improving confidence in assessment of anthropogenic contributions and climate change projections.

Gaseous emissions from vehicles contribute substantially to air pollution and climate change. Vehicular emissions also contain secondary pollutants produced via chemical reactions that occur between the emitted gases and atmospheric air. This study aims at understanding patterns concerning emission of regulated, greenhouse, and precursor gases, which demonstrate potential for secondary aerosol (SA) formation, from vehicles incorporating different engine technologies—multi-point injection (MPI) and gasoline direct injection (GDI)—and using different fuels—gasoline, liquefied petroleum gas (LPG), and diesel. Drive cycles from the National Institute of Environmental Research (NIER) were used in this study. Results obtained from drive cycle tests demonstrate a decline in aggregate gas emissions corresponding to an increase in average vehicle speed. CO₂ accounts for more than 99% of aggregate gaseous emissions. In terms of concentration, CO and NH₃ form predominantly non-CO₂ emissions from gasoline and LPG vehicles, whereas nitrogen oxides (NOx) and non-methane hydrocarbons (NMHC) dominate diesel-vehicle emissions. A higher percentage of SO₂ is emitted from diesel vehicles compared to their gasoline- and LPG-powered counterparts. EURO-5- and EURO-6-compliant vehicles equipped with diesel particulate filters (DPFs) tend to emit higher amounts of NO₂ compared to EURO-3-compliant vehicles, which are not equipped with DPFs. Vehicles incorporating GDI tend to emit less CO₂ compared to those incorporating MPI, albeit at the expense of increased CO emissions. The authors believe that results reported in this paper concerning regulated and unregulated pollutant-emission monitoring can contribute towards an accurate evaluation of both primary and secondary air-pollution scenarios in Korea.

We characterized residential biomass burning contributions to fine particle concentrations via multiple methods at Fyfe Elementary School in Las Vegas, Nevada, during January 2008: with levoglucosan on quartz fiber filters; with water soluble potassium (K⁺) measured using a particle-into-liquid system with ion chromatography (PILS-IC); and with the fragment C₂H₄O₂⁺ from an Aerodyne High Resolution Aerosol Mass Spectrometer (HR-AMS). A Magee Scientific Aethalometer was also used to determine aerosol absorption at the UV (370 nm) and black carbon (BC, 880 nm) channels, where UV-BC difference is indicative of biomass burning (BB). Levoglucosan and AMS C₂H₄O₂⁺ measurements were strongly correlated (r² = 0.92); K⁺ correlated well with C₂H₄O₂⁺ (r² = 0.86) during the evening but not during other times. While K⁺ may be an indicator of BB, it is not necessarily a unique tracer, as non-BB sources appear to contribute significantly to K⁺ and can change from day to day. Low correlation was seen between UV-BC difference and other indicators, possibly because of an overwhelming influence of freeway emissions on BC concentrations. Given the sampling location—next to a twelve-lane freeway—urban-scale biomass burning was found to be a surprisingly large source of aerosol: overnight BB organic aerosol contributed between 26% and 33% of the organic aerosol mass.

Atmospheric ammonia is a significant pollutant throughout the year, necessitating standardized measurement and identification of emission factors. We performed a quantized evaluation of ammonia concentrations at various locations in and around Seoul, South Korea. The established testing methods of the Radiello Passive Sampler were used for ammonia sampling, and the method was validated using annular denuder sampling. Urban and suburban areas were studied to gain a deeper understanding of the factors responsible for ammonia pollution. This study aimed to establish the fluctuations in concentration over one year, by analyzing the seasonal and regional variation in ammonia concentration. Livestock and agricultural areas recorded the highest concentration of ammonia among all sites, with the highest concentration recorded in autumn. However, at most of the other studied sites, the highest and lowest ammonia concentrations were recorded during summer and winter, respectively. This study attempted to establish a correlation between ammonia concentration and temperature, as well as ammonia concentration and altitude.