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Graph‐theoretical methods have revolutionized the exploration of complex systems across scientific disciplines. Here, we demonstrate their applicability to the investigation and comparison of three widely used atmospheric chemical mechanisms of varying complexity: the Master Chemical Mechanism v3.3, GEOS‐Chem v12.6, and the Super‐Fast chemical mechanism. We investigate these mechanisms using a class of graphical models known as species‐reaction graphs and find similarities between these chemical reaction systems and other systems arising in nature. Several graph theoretical properties are consistent across mechanisms, including strong dynamical system disequilibrium and clustering of chemically related species. This formalism also reveals key differences between the mechanisms, some of which have characteristics inconsistent with domain knowledge; e.g., isoprene and peroxy radical chemistry exhibit substantially different graph properties in each mechanism. Graph‐theoretical methods provide a promising set of tools for investigating atmospheric chemical mechanisms, complementing existing computational approaches, and potentially opening new avenues for scientific discovery. Plain Language Summary: A type of math known as "graph theory" provides a set of tools for scientific discovery in a variety of contexts. These include the study of social networks, the power grid, and the human brain. In this paper, we apply these tools to the study of the chemical reactions that occur in the atmosphere. We investigate and intercompare several different descriptions of these reactions, and find that these new graph theory‐based techniques provide information consistent with existing scientific techniques, and potentially allow for new avenues for scientific discovery. Key Points: We investigate three atmospheric chemical mechanisms of varying complexity and process representation using graph‐theoretical techniquesResults are consistent with existing computational approaches, demonstrating key similarities and differences between the mechanismsThese techniques provide chemical state independent, structural insights into existing mechanisms, and methods for new mechanism development [ABSTRACT FROM AUTHOR]

Ice nucleating particles (INPs) are a rare subset of particles that can have an outsized impact relative to their prevalence. To simulate INP variability, models require parameterizations for the most important sources of INPs. Most parameterizations in the literature were developed from laboratory experiments that used proxies for ambient particles. Whether these laboratory models effectively represent ambient aerosol is still not well understood and parameterizations developed from field measurements may be more atmospherically relevant. Expanding on previous work in which we used an ice chamber and a single‐particle mass spectrometer (SPMS) to characterize INP composition, we calculate heterogeneous ice nucleation rate coefficients for ambient particles. We find good agreement between our dust and previous measurements of airborne dust. We also use a Monte Carlo approach to assess the relative contribution of each measurement to the uncertainty and find that the biggest source is the sampling efficiency of the SPMS. Plain Language Summary: Ice nucleation activity is typically parameterized using results from laboratory experiments. However, whether these parameterizations accurately represent the ice nucleation activity of ambient aerosol is not well understood. In this work, we develop a novel framework for deriving ice nucleation rate coefficients from ambient measurements, and apply these methods to an ambient data set using a Monte Carlo approach. Key Points: An integrated CFDC and ATOFMS were used to derive particle‐type specific heterogeneous ice nucleation rate coefficients for ambient aerosolDust ice nucleation rate coefficients were within 2–5‐fold of previous estimatesThese are the first published measurements of nucleation rate coefficients for sea spray aerosol [ABSTRACT FROM AUTHOR]