Parameterized Yields of Semivolatile Products from Isoprene Oxidation under Different NO_<i>x</i> Levels: Impacts of Chemical Aging and Wall-Loss of Reactive Gases

We developed a parametrizable box model to empirically derive the yields of semivolatile products from VOC oxidation using chamber measurements, while explicitly accounting for the multigenerational chemical aging processes (such as the gas-phase fragmentation and functionalization and aerosol-phase oligomerization and photolysis) under different NO_<i>x</i> levels and the loss of particles and gases to chamber walls. Using the oxidation of isoprene as an example, we showed that the assumptions regarding the NO_<i>x</i>-sensitive, multigenerational aging processes of VOC oxidation products have large impacts on the parametrized product yields and SOA formation. We derived sets of semivolatile product yields from isoprene oxidation under different NO_<i>x</i> levels. However, we stress that these product yields must be used in conjunction with the corresponding multigenerational aging schemes in chemical transport models. As more mechanistic insights regarding SOA formation from VOC oxidation emerge, our box model can be expanded to include more explicit chemical aging processes and help ultimately bridge the gap between the process-based understanding of SOA formation from VOC oxidation and the bulk-yield parametrizations used in chemical transport models

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing −5–6 μg·m–3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes