Computer-aided estimation of kinetic rate constant for degradation of volatile organic compounds by hydroxyl radical: An improved model using quantum chemical and norm descriptors

Control of odorants in swine manure and food waste co-composting via zero-valent iron /H2O2 system

Odors have posed challenges to the advancement of aerobic composting. This work aims to identify the primary components responsible for odors and assess the effectiveness and mechanisms of the zero-valent iron/H2O2 system controlling various odorants in aerobic composting. Swine manure and food waste were used as composting materials, with the addition of zero-valent iron and hydrogen peroxide to mitigate odor emissions. Results revealed that odorants included ammonia, hydrogen sulfide, and 22 types of volatile organic compounds (VOCs), with ethyl acetate, heptane, and dimethyl disulfide being predominant. Among the odorants emitted, ammonia accounted for 75.43%, hydrogen sulfide for 0.09%, and identified VOCs for 24.48%. The ZVI/H2O2 system showed a significant reduction in ammonia and VOCs emission, with the reduction of 51% (ammonia) and 41.3% (VOCs) respectively, primarily observed during the thermophilic period. The occurrence of Fenton-like reactions and changes in key microbial populations were the main mechanisms accounting for odor control. The occurrence of Fenton-like reaction was confirmed by X-ray photoelectron spectroscopy and reactive oxygen detection, showing the oxidation of zero-valent iron by H2O2 to higher valence elemental iron, and the simultaneous production of ·OH. Microbial analysis indicated that an enrichment of specific microorganisms with Bacillus contributed to feammonx and Bacillaceae contributed to organic biodegradation. Redundancy analysis highlighted the role of key microbial species (Bacillaceae, Bacillus, and Ureibacillus) in effectively reducing the level of ammonia and volatile organic compounds. These novelty findings illustrated that the potential of this system is promising for controlling the emission of odorants and aerobic composting reinforcement.

Predicting reactivity dynamics of halogen species and trace organic contaminants using machine learning models

Reactions of reactive halogen species (Cl•, Br•, and Cl2•-) with trace organic contaminants (TrOCs) have received much attention in recent years, and their k values are fundamental parameters for understanding their reaction mechanisms. However, k values are usually unknown. In this study, we developed machine learning (ML)-based quantitative structure-activity relationship (QSAR) models to predict k values. We tested five algorithms, namely, random forest, neural network, XGBoost, support vector machine (SVM), and multilinear regression, using molecular descriptors (MDs) and molecular fingerprints (MFs) as inputs. The optimal algorithms were MD-XGBoost for Cl• and Br•, and MF-SVM for Cl2•-, respectively, with R2test values of 0.876, 0.743, and 0.853. We found that electron-withdrawing/donating groups tended to interfere with the reactivity of Cl2•- more than Cl• and Br•. This explains why MFs are better inputs for predictive models of Cl2•-, whereas MDs are more suitable for Cl• and Br•. Furthermore, we interpreted the models using SHAP analysis, and the results indicated that our models accurately predicted k values both statistically and mechanistically. Our models provide useful tools for obtaining unknown k values and help researchers understand the inherent relationships between the models.

Transfer Learning Approach to Multitarget Temperature-Dependent Reaction Rate Prediction

Accurate prediction of temperature-dependent reaction rate constants of organic compounds is of great importance to both atmospheric chemistry and combustion science. Extensive work has been done on developing automated mechanism generation systems but the lack of quality reaction rate data remains a huge bottleneck in the application of highly detailed mechanisms. Machine learning prediction models have been recently adopted to alleviate the data gap in thermochemistry and have great potential to do the same for kinetic data with the recent release of quality reaction rate data compilations. The ultimate goal is to formulate easily accessible, general-purpose, temperature-dependent, and multitarget models for the prediction of reaction rates. To that end, we propose a model that depends on the well-known Morgan fingerprints as well as learned representations transferred from the QM9 data set. We propose the use of an Arrhenius-based loss where predictions of the three modified-Arrhenius parameters (A, n, and B = E_a/R) are given instead of the direct prediction of reaction rate constants. Our model is >35% more accurate compared to a baseline model of feed forward network (FFN) on Morgan fingerprints.