Biotransformation of chemical contaminants is of importance in various natural and engineered systems. However, in complex microbial communities and with chemical contaminants at low concentrations, our current understanding of biotransformation at the level of enzyme-chemical interactions is limited. Here, we explored an approach to identify associations between micropollutant biotransformation and specific gene products in complex microbial communities, using association mining between chemical and metatranscriptomic data obtained from experiments with activated sludge grown at different solids retention times. We successfully demonstrate proportional relationships between the measured rate constants and associated gene transcripts for nitrification as a major community function, but also for the biotransformation of two nitrile-containing micropollutants (bromoxynil and acetamiprid) and transcripts of nitrile hydratases, a class of enzymes that we experimentally confirmed to produce the detected amide transformation products. Since these results suggest that metatranscriptomic information can indeed be quantitatively correlated with low abundant community functions such as micropollutant biotransformation in complex microbial communities, we proceeded to explore the potential of association mining to highlight enzymes likely involved in catalyzing less well-understood micropollutant biotransformation reactions. Specifically, we use the cases of nitrile hydration and oxidative biotransformation reactions to show that the consideration of additional experimental evidence (such as information on biotransformation pathways) increases the likelihood of detecting plausible novel enzyme-chemical relationships. Finally, we identify a cluster of mono-and dioxygenase fourth-level enzyme classes that most strongly correlate with oxidative micropollutant biotransformation reactions in activated sludge.

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,...

Abstract. The heterogeneous chemistry of atmospheric aerosols involves multiphase chemical kinetics that can be described by kinetic multi-layer models (KMs) that explicitly resolve mass transport and chemical reactions. However, KMs are computationally too expensive to be used as sub-modules in large-scale atmospheric models, and the computational costs also limit their utility in inverse-modeling approaches commonly used to infer aerosol kinetic parameters from laboratory studies. In this study, we show how machine learning methods can generate inexpensive surrogate models for the kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB) to predict reaction times in multiphase chemical systems. We apply and compare two common and openly available methods for the generation of surrogate models, polynomial chaos expansion (PCE) with UQLab and neural networks (NNs) through the Python package Keras. We show that the PCE method is well suited to determining global sensitivity indices of the KMs, and we demonstrate how inverse-modeling applications can be enabled or accelerated with NN-suggested sampling. These qualities make them suitable supporting tools for laboratory work in the interpretation of data and the design of future experiments. Overall, the KM surrogate models investigated in this study are fast, accurate, and robust, which suggests their applicability as sub-modules in large-scale atmospheric models.

Abstract. The heterogeneous chemistry of atmospheric aerosols involves multiphase chemical kinetics that can be described by kinetic multi-layer models (KM) explicitly resolving mass transport and chemical reaction. However, KM are computationally too expensive to be used as sub-modules in large-scale atmospheric models, and the computational costs also limit their utility in inverse modelling approaches commonly used to infer aerosol kinetic parameters from laboratory studies. In this study, we show how machine learning methods can generate inexpensive surrogate models based on the kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB). We apply and compare two common and openly available methods for the generation of surrogate models, polynomial chaos expansion (PCE) with UQLab and neural networks (NN) through the Python package Keras. We show that the PCE method is well-suited to determine global sensitivity indices of the KM and demonstrate how inverse modelling applications can be enabled or accelerated with NN-suggested sampling. These qualities make them suitable supporting tools for laboratory work in the interpretation of data and design of future experiments. Overall, the KM surrogate models investigated in this study are fast, accurate, and robust, which suggests their applicability as sub-modules in large-scale atmospheric models.

Unsaturated organic compounds in aerosol particles undergo oxidative ageing via heterogeneous reactions with atmospheric oxidants. Ozonolysis of linoleic acid has been serving as a proxy for this process and the...