Wildfire plume ageing in the Photochemical Large Aerosol Chamber (PHOTO-LAC).

Plumes from wildfires are transported over large distances from remote to populated areas and threaten sensitive ecosystems. Dense wildfire plumes are processed by atmospheric oxidants and complex multiphase chemistry, differing from processes at typical ambient concentrations. For studying dense biomass burning plume chemistry in the laboratory, we establish a Photochemical Large Aerosol Chamber (PHOTO-LAC) being the world's largest aerosol chamber with a volume of 1800 m3 and provide its figures of merit. While the photolysis rate of NO2 (jNO2) is comparable to that of other chambers, the PHOTO-LAC and its associated low surface-to-volume ratio lead to exceptionally low losses of particles to the walls. Photochemical ageing of toluene under high-NOx conditions induces substantial formation of secondary organic aerosols (SOAs) and brown carbon (BrC). Several individual nitrophenolic compounds could be detected by high resolution mass spectrometry, demonstrating similar photochemistry to other environmental chambers. Biomass burning aerosols are generated from pine wood and debris under flaming and smouldering combustion conditions and subsequently aged under photochemical and dark ageing conditions, thus resembling day- and night-time atmospheric chemistry. In the unprecedented long ageing with alternating photochemical and dark ageing conditions, the temporal evolution of particulate matter and its chemical composition is shown by ultra-high resolution mass spectrometry. Due to the spacious cavity, the PHOTO-LAC may be used for applications requiring large amounts of particulate matter, such as comprehensive chemical aerosol characterisation or cell exposures under submersed conditions.

PyC2MC: An Open-Source Software Solution for Visualization and Treatment of High-Resolution Mass Spectrometry Data.

Complex molecular mixtures are encountered in almost all research disciplines, such as biomedical 'omics, petroleomics, and environmental sciences. State-of-the-art characterization of sample materials related to these fields, deploying high-end instrumentation, allows for gathering large quantities of molecular composition data. One established technological platform is ultrahigh-resolution mass spectrometry, e.g., Fourier-transform mass spectrometry (FT-MS). However, the huge amounts of data acquired in FT-MS often result in tedious data treatment and visualization. FT-MS analysis of complex matrices can easily lead to single mass spectra with more than 10,000 attributed unique molecular formulas. Sophisticated software solutions to conduct these treatment and visualization attempts from commercial and noncommercial origins exist. However, existing applications have distinct drawbacks, such as focusing on only one type of graphic representation, being unable to handle large data sets, or not being publicly available. In this respect, we developed a software, within the international complex matrices molecular characterization joint lab (IC2MC), named "python tools for complex matrices molecular characterization" (PyC2MC). This piece of software will be open-source and free to use. PyC2MC is written under python 3.9.7 and relies on well-known libraries such as pandas, NumPy, or SciPy. It is provided with a graphical user interface developed under PyQt5. The two options for execution, (1) a user-friendly route with a prepacked executable file or (2) running the main python script through a Python interpreter, ensure a high applicability but also an open characteristic for further development by the community. Both are available on the GitHub platform (https://github.com/iC2MC/PyC2MC_viewer).