Modeling the Dynamics of Mixture Toxicity and Effects of Organic Micropollutants in a Small River under Unsteady Flow Conditions.

The presence of anthropogenic organic micropollutants in rivers poses a long-term threat to surface water quality. To describe and quantify the in-stream fate of single micropollutants, the advection-dispersion-reaction (ADR) equation has been used previously. Understanding the dynamics of the mixture effects and cytotoxicity that are cumulatively caused by micropollutant mixtures along their flow path in rivers requires a new concept. Thus, we extended the ADR equation from single micropollutants to defined mixtures and then to the measured mixture effects of micropollutants extracted from the same river water samples. Effects (single and mixture) are expressed as effect units and toxic units, the inverse of effect concentrations and inhibitory concentrations, respectively, quantified using a panel of in vitro bioassays. We performed a Lagrangian sampling campaign under unsteady flow, collecting river water that was impacted by a wastewater treatment plant (WWTP) effluent. To reduce the computational time, the solution of the ADR equation was expressed by a convolution-based reactive transport approach, which was used to simulate the dynamics of the effects. The dissipation dynamics of the individual micropollutants were reproduced by the deterministic model following first-order kinetics. The dynamics of experimental mixture effects without known compositions were captured by the model ensemble obtained through Bayesian calibration. The highly fluctuating WWTP effluent discharge dominated the temporal patterns of the effect fluxes in the river. Minor inputs likely from surface runoff and pesticide diffusion might contribute to the general effect and cytotoxicity pattern but could not be confirmed by the model-based analysis of the available effect and chemical data.

Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?

Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can be quantitatively linked to reaction rates remains an open question. We present an enzyme-based denitrification model that simulates concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes. We calibrated the model using experimental data from a well-controlled batch experiment with the denitrifier Paracoccous denitrificans. The model accurately predicts denitrification rates and measured transcript dynamics. The relationship between simulated transcript concentrations and reaction rates exhibits strong non-linearity and hysteresis related to the faster dynamics of gene transcription and substrate consumption, relative to enzyme production and decay. Hence, assuming a unique relationship between transcript-to-gene ratios and reaction rates, as frequently suggested, may be an erroneous simplification. Comparing model results of our enzyme-based model to those of a classical Monod-type model reveals that both formulations perform equally well with respect to nitrogen species, indicating only a low benefit of integrating molecular-biological data for estimating denitrification rates. Nonetheless, the enzyme-based model is a valuable tool to improve our mechanistic understanding of the relationship between biomolecular quantities and reaction rates. Furthermore, our results highlight that both enzyme kinetics (i.e., substrate limitation and inhibition) and gene expression or enzyme dynamics are important controls on denitrification rates.