Research Highlights

New particle formation (NPF) is a major source of atmospheric nanoparticles that affect aerosol populations, air quality, human health, and the atmosphere. The complex and nonlinear interactions among radiation, gases, and meteorology make it difficult to pinpoint what conditions trigger events that form new particles. In this study, researchers applied a machine learning technique (random forest) to long-term atmospheric measurements in a rural continental environment to classify NPF and non‑NPF days and to identify which environmental factors matter most. The approach captures the intricate relationships that traditional methods often miss and provides a quantitative ranking of the controlling variables.

Prior laboratory and modelling studies have indicated that trace gases can affect the critical supersaturation required to activate a particle into a cloud droplet. This study represents the first investigation of the hypothesis that semivolatile trace gases influence cloud condensation nuclei activity in the atmosphere using field observations. To this end, we compared size-resolved activation supersaturation with and without trace gases. Activation supersaturation increased without trace gases when aerosols were wet (size-selected at higher relative humidity). This may be due to loss of semivolatiles by evaporation from the particle phase. Activation supersaturation decreased after removal of trace gases when trace gas concentrations were higher. This may be due to a change in interfacial tension by trace gas adsorption.

We analyzed characteristics of pinned clouds, industrial heat sources that trigger them, and the conditions in which they form.