A numerical compass for experiment design in chemical kinetics and molecular property estimation.

Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure-activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass.

Endogenous Nitric Oxide Can Enhance Oxidative Stress Caused by Air Pollutants and Explain Higher Susceptibility of Individuals with Inflammatory Disorders.

Air pollution causes morbidity and excess mortality. In the epithelial lining fluid of the respiratory tract, air pollutants trigger a chemical reaction sequence that causes the formation of noxious hydroxyl radicals that drive oxidative stress. For hitherto unknown reasons, individuals with pre-existing inflammatory disorders are particularly susceptible to air pollution. Through detailed multiphase chemical kinetic analysis, we show that the commonly elevated concentrations of endogenous nitric oxide in diseased individuals can increase the production of hydroxyl radicals via peroxynitrite formation. Our findings offer a molecular rationale of how adverse health effects and oxidative stress caused by air pollutants may be exacerbated by inflammatory disorders.

Multiphase Kinetic Modeling of Air Pollutant Effects on Protein Modification and Nitrotyrosine Formation in Epithelial Lining Fluid.

Exposure to ambient air pollution is a major risk factor for human health. Inhalation of air pollutants can enhance the formation of reactive species in the epithelial lining fluid (ELF) of the respiratory tract and can lead to oxidative stress and oxidative damage. Here, we investigate the chemical modification of proteins by reactive species from air pollution and endogenous biological sources using an extended version of the multiphase chemical kinetic model KM-SUB-ELF 2.0 with a detailed mechanism of protein modification. Fine particulate matter (PM2.5) and nitrogen dioxide (•NO2) act synergistically and increase the formation of nitrotyrosine (Ntyr), a common biomarker of oxidative stress. Ozone (O3) is found to be a burden on the antioxidant defense system but without substantial influence on the Ntyr concentration. In simulations with low levels of air pollution, the Ntyr concentration in the ELF is consistent with the range of literature values for bronchoalveolar lavage fluid from healthy individuals. With high levels of air pollution, however, we obtain strongly elevated Ntyr concentrations. Our model analysis shows how chemical reactions of air pollutants can modify proteins and thus their functionality in the human body, elucidating a molecular pathway that may explain air pollutant effects on human health.

Electrodynamic balance-mass spectrometry reveals impact of oxidant concentration on product composition in the ozonolysis of oleic acid.

The chemical and physical properties of atmospheric aerosol particles change upon oxidative ageing, influencing their interaction with radiation, their propensity to serve as nuclei for cloud condensation and ice formation, and their adverse effects on human health. The investigation of atmospheric aerosol oxidation processes is complicated by low oxidant concentrations and long timescales, which are difficult to represent in laboratory studies. Experimental work often attempts to compensate for short timescales with elevated concentrations of oxidative agents, assuming that the ageing progress depends only on the oxidant exposure, i.e. on the product of oxidant concentration and time, [Ox] × t, and not on [Ox] or t independently. The application of electrodynamic balance-mass spectrometry of single particles allows the validity of this assumption to be investigated, since it provides information on the molecular composition of aerosol particles for a wide range of reaction durations under well-defined oxidation conditions. Here, we demonstrate the capabilities of a new setup on levitated oleic acid droplets reacting with ozone at mixing ratios of 0.2 and 15 ppm, i.e. spanning almost two orders of magnitude in [Ox]. We show that the reactive removal of oleic acid can be accurately expressed as a function of ozone exposure [Ox] × t, whereas the product concentrations depend on [Ox] and t independently. As the underlying reason for the breakdown of the exposure metric, we suggest a competition between evaporation of volatile first-generation products and their accretion reactions with reactive Criegee intermediates, converting them into low-volatility dimers and oligomers. This hypothesis is supported by kinetic model simulations using the aerosol process model KM-SUB, which explicitly resolves the competition between evaporation and secondary chemistry as a function of the experimental timescale and ozone mixing ratio. The model successfully reproduces final product distributions. The findings are further supported by the recorded changes of droplet sizes during oxidation. As a heuristic, the breakdown of the exposure metric in a chemical reaction system is possible, when competition between first- and second-order processes of reactive intermediates determines important system properties.

Hydroxyl Radical Production by Air Pollutants in Epithelial Lining Fluid Governed by Interconversion and Scavenging of Reactive Oxygen Species.

Air pollution is a major risk factor for human health. Chemical reactions in the epithelial lining fluid (ELF) of the human respiratory tract result in the formation of reactive oxygen species (ROS), which can lead to oxidative stress and adverse health effects. We use kinetic modeling to quantify the effects of fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) on ROS formation, interconversion, and reactivity, and discuss different chemical metrics for oxidative stress, such as cumulative production of ROS and hydrogen peroxide (H2O2) to hydroxyl radical (OH) conversion. All three air pollutants produce ROS that accumulate in the ELF as H2O2, which serves as reservoir for radical species. At low PM2.5 concentrations (<10 µg m-3), we find that less than 4% of all produced H2O2 is converted into highly reactive OH, while the rest is intercepted by antioxidants and enzymes that serve as ROS buffering agents. At elevated PM2.5 concentrations (>10 µg m-3), however, Fenton chemistry overwhelms the ROS buffering effect and leads to a tipping point in H2O2 fate, causing a strong nonlinear increase in OH production. This shift in ROS chemistry and the enhanced OH production provide a tentative mechanistic explanation for how the inhalation of PM2.5 induces oxidative stress and adverse health effects.