Dynamic polluted atmosphere generator at low ppbv levels for validating VOC sampling methods

Prediction of the Response of a Photoionization Detector to a Complex Gaseous Mixture of Volatile Organic Compounds Produced by α-Pinene Oxidation

Photoionization detectors (PIDs) are lightweight and respond in real time to the concentrations of volatile organic compounds (VOCs), making them suitable for environmental measurements on many platforms. However, the nonselective sensing mechanism of PIDs challenges data interpretation, particularly when exposed to the complex VOC mixtures prevalent in the Earth's atmosphere. Herein, two approaches to this challenge are investigated. In the first, quantum-chemistry calculations are used to estimate photoionization cross sections and ionization potentials of individual species. In the second, machine learning models are trained on these calculated values, as well as empirical PID response factors, and then used for prediction. For both approaches, the resulting information for individual species is used to model the overall PID response to a complex VOC mixture. In complement, laboratory experiments in the Harvard Environmental Chamber are carried out to measure the PID response to the complex molecular mixture produced by α-pinene oxidation under various conditions. The observations show that the measured PID response is 15% to 30% smaller than the PID response modeled by quantum-chemistry calculations of the photoionization cross section for the photo-oxidation experiments and 15% to 20% for the ozonolysis experiments. By comparison, the measured PID response is captured within a 95% confidence interval by the use of machine learning to model the PID response based on the empirical response factor in all experiments. Taken together, the results of this study demonstrate the application of machine learning to augment the performance of a nonselective chemical sensor. The approach can be generalized to other reactive species, oxidants, and reaction mechanisms, thus enhancing the utility and interpretability of PID measurements for studying atmospheric VOCs.

An investigation of the adsorption of C5-C12 hydrocarbons in the ppmv and ppbv ranges on Carbotrap B

The effects of concentration and temperature on the breakthrough volumes (Vb) of 23 volatile organic compounds on Carbotrap B have been determined using the frontal chromatography method. From the measured Vb, original isotherms have been produced and adsorption parameters based on the Langmuir, Freundlich, and Dubinin-Polyani adsorption models have been calculated. The calculated adsorption parameters describe the behavior of these VOC on Carbotrap B under the experimental conditions and are useful data for VOC sampling applications including adsorption modeling of pumped and diffusive sampling. Each of the adsorption models give similar results and are in good agreement with the experimental data in the ppmv concentration range. It will be shown that contrary to previous assumptions the Langmuir adsorption parameters obtained at ppmv concentrations cannot be used to predict Vb at ppbv concentrations and the calculated parameter mmax does not represent the maximum adsorbent capacity. The Freundlich and Dubinin-Polyani models are shown to be more successful in describing the adsorption behavior of the VOC at ppbv levels where Vb is independent of concentration. The isosteric heats of adsorption (-delta Hst) for some of the compounds have been determined using the Van't Hoff equation which can be used to predict the effect of temperature on Vb.