Impact of dry-wet and freeze-thaw events on pesticide mineralizing populations and their activity in wetland ecosystems: A microcosm study

Vandermeeren, Pieter; Baken, Stijn; Vanderstukken, Ruben; Diels, Jan; Springael, Dirk

doi:10.1016/j.chemosphere.2015.11.089

Cited by 13 publications

(5 citation statements)

References 67 publications

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“…Mineralization kinetics were described using the modified Gompertz model where P (%) is the percentage mineralization at time t (h), A (%) the total extent of mineralization after the exponential mineralization phase, λ (h) is the lag time, μ (% h –1 ) is the maximum mineralization rate constant, and c (% h –1 ) is the endogenous mineralization rate constant. Parameters were determined using the lsqnonlin command in Matlab R2012b (MathWorks, Natick, MA, USA) with the default trust-region reflective algorithm, at a termination tolerance of 10 –14 and allowing maximum 200,000 function evaluations and 30,000 iterations . The values of c were always close to zero.…”

Section: Methodsmentioning

confidence: 99%

Interspecies Interactions of the 2,6-Dichlorobenzamide Degrading Aminobacter sp. MSH1 with Resident Sand Filter Bacteria: Indications for Mutual Cooperative Interactions That Improve BAM Mineralization Activity

Vandermaesen

Daly

et al. 2022

Environ. Sci. Technol.

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Bioaugmentation often involves an invasion process requiring the establishment and activity of a foreign microbe in the resident community of the target environment. Interactions with resident micro-organisms, either antagonistic or cooperative, are believed to impact invasion. However, few studies have examined the variability of interactions between an invader and resident species of its target environment, and none of them considered a bioremediation context. Aminobacter sp. MSH1 mineralizing the groundwater micropollutant 2,6-dichlorobenzamide (BAM), is proposed for bioaugmentation of sand filters used in drinking water production to avert BAM contamination. We examined the nature of the interactions between MSH1 and 13 sand filter resident bacteria in dual and triple species assemblies in sand microcosms. The residents affected MSH1-mediated BAM mineralization without always impacting MSH1 cell densities, indicating effects on cell physiology rather than on cell number. Exploitative competition explained most of the effects (70%), but indications of interference competition were also found. Two residents improved BAM mineralization in dual species assemblies, apparently in a mutual cooperation, and overruled negative effects by others in triple species systems. The results suggest that sand filter communities contain species that increase MSH1 fitness. This opens doors for assisting bioaugmentation through co-inoculation with “helper” bacteria originating from and adapted to the target environment.

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Section: Methodsmentioning

confidence: 99%

Interspecies Interactions of the 2,6-Dichlorobenzamide Degrading Aminobacter sp. MSH1 with Resident Sand Filter Bacteria: Indications for Mutual Cooperative Interactions That Improve BAM Mineralization Activity

Vandermaesen

Daly

et al. 2022

Environ. Sci. Technol.

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“…The Gompertz parameters of the cumulative mineralization curves were determined by least-squares curve fitting, using the trust-region-reflective algorithm, , at a termination tolerance of 10 –14 and allowing at most 2 × 10 5 function evaluations and 3 × 10 5 iterations. Initial parameter estimates were set at 30, 5, and 2 for A , μ, and λ, respectively . The reflective algorithm was implemented using Matlab R2012b (Mathworks).…”

Section: Methodsmentioning

confidence: 99%

“…Initial parameter estimates were set at 30, 5, and 2 for A, μ, and λ, respectively. 30 The reflective algorithm was implemented using Matlab R2012b (Mathworks).…”

Section: Methodsmentioning

confidence: 99%

Guiding Mineralization Co-Culture Discovery Using Bayesian Optimization

Daly

Stock

Baetens

et al. 2019

Environ. Sci. Technol.

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Many disciplines rely on testing combinations of compounds, materials, proteins, or bacterial species to drive scientific discovery. It is timeconsuming and expensive to determine experimentally, via trial-and-error or random selection approaches, which of the many possible combinations will lead to desirable outcomes. Hence, there is a pressing need for more rational and efficient experimental design approaches to reduce experimental effort. In this work, we demonstrate the potential of machine learning methods for the in silico selection of promising co-culture combinations in the application of bioaugmentation. We use the example of pollutant removal in drinking water treatment plants, which can be achieved using co-cultures of a specialized pollutant degrader with combinations of bacterial isolates. To reduce the experimental effort needed to discover high-performing combinations, we propose a data-driven experimental design. Based on a dataset of mineralization performance for all pairs of 13 bacterial species co-cultured with MSH1, we built a Gaussian process regression model to predict the Gompertz mineralization parameters of the co-cultures of two and three species, based on the single-strain parameters. We subsequently used this model in a Bayesian optimization scheme to suggest potentially high-performing combinations of bacteria. We achieved good performance with this approach, both for predicting mineralization parameters and for selecting effective co-cultures, despite the limited dataset. As a novel application of Bayesian optimization in bioremediation, this experimental design approach has promising applications for highlighting co-culture combinations for in vitro testing in various settings, to lessen the experimental burden and perform more targeted screenings.

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“…From the current knowledge, vegetated buffer systems have the potential to reduce pesticides leaching and retain up to 90% in runoff. However, based on limited monitoring data, there is uncertainty to quantify the biodegradation effectiveness of RBZ in most cases (Entry et al, 1994;Vandermeeren et al, 2016).…”

Section: Agro-omps Removal Efficiencymentioning

confidence: 99%

Remediation potential of agricultural organic micropollutants in in-situ techniques: A review

Bai

Wang

Hoek

2022

Ecological Informatics

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Impact of dry-wet and freeze-thaw events on pesticide mineralizing populations and their activity in wetland ecosystems: A microcosm study

Cited by 13 publications

References 67 publications

Interspecies Interactions of the 2,6-Dichlorobenzamide Degrading Aminobacter sp. MSH1 with Resident Sand Filter Bacteria: Indications for Mutual Cooperative Interactions That Improve BAM Mineralization Activity

Interspecies Interactions of the 2,6-Dichlorobenzamide Degrading Aminobacter sp. MSH1 with Resident Sand Filter Bacteria: Indications for Mutual Cooperative Interactions That Improve BAM Mineralization Activity

Guiding Mineralization Co-Culture Discovery Using Bayesian Optimization

Remediation potential of agricultural organic micropollutants in in-situ techniques: A review

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