Hydrogen (H₂) is a gas in trace amounts in the Earth’s atmosphere with indirect impacts on climate and air quality. Renewed interest in H₂ as a low- or zero-carbon source of energy may lead to increased production, uses, and supply chain emissions. NOAA measurements of weekly air samples collected between 2009 and 2021 at over 50 sites in mostly remote locations are now available, and they complement other datasets to study the H₂ global budget.

Recent advances in high-resolution time-of-flight chemical ionization mass spectrometry (CIMS) enable the detection of highly oxygenated organic molecules, which efficiently contribute to secondary organic aerosol. Here we present an application of fuzzy c-means (FCM) clustering  to deconvolve CIMS data. FCM not only reduces the complexity of mass spectrometric data but also the chemical and kinetic information retrieved by clustering gives insights into the chemical processes involved.

We experimentally investigated vapor wall loss of n-alkanes in a Teflon chamber across a wide temperature range. Increased wall loss was observed at lower temperatures. Further analysis suggests that lower temperatures enhance partitioning of n-alkanes to the surface layer of a Teflon wall but slow their diffusion into the inner layer. The results are important for quantitative analysis of chamber experiments conducted at low temperatures, simulating wintertime or upper-tropospheric conditions.

We optimized and conducted an experimental program for the real-time monitoring of non-methane hydrocarbon instruments using the direct method. Changing the enrichment and specially designed columns further improved the test effect. The results correct the measurement errors that have prevailed for many years and can lay a foundation for the evaluation of volatile organic compounds in the regional ambient air and provide direction for the measurement of low-concentration ambient air pollutants.

A network of low-cost sensors is a good alternative to improve the detection of fugitive CH₄ emissions. We present the results of four tests conducted with two types of Figaro sensors that were assembled on four chambers in a laboratory experiment: a comparison of five models to reconstruct the CH₄ signal, a strategy to reduce the training set size, a detection of age effects in the sensors and a test of the capability to transfer a model between chambers for the same type of sensor.