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  1. Article ; Online: Greenhouse gas production from an intermittently dosed cold-climate wastewater treatment wetland.

    Ayotte, S H / Allen, C R / Parker, A / Stein, O R / Lauchnor, E G

    The Science of the total environment

    2024  Volume 924, Page(s) 171484

    Abstract: This study explores the greenhouse gas (GHG) fluxes of nitrous oxide ( ... ...

    Abstract This study explores the greenhouse gas (GHG) fluxes of nitrous oxide (N
    MeSH term(s) Greenhouse Gases/analysis ; Wetlands ; Carbon Dioxide/analysis ; Greenhouse Effect ; Wastewater ; Environmental Monitoring ; Nitrogen ; Methane/analysis ; Nitrous Oxide/analysis ; Water Purification
    Chemical Substances Greenhouse Gases ; Carbon Dioxide (142M471B3J) ; Wastewater ; Nitrogen (N762921K75) ; Methane (OP0UW79H66) ; Nitrous Oxide (K50XQU1029)
    Language English
    Publishing date 2024-03-09
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 121506-1
    ISSN 1879-1026 ; 0048-9697
    ISSN (online) 1879-1026
    ISSN 0048-9697
    DOI 10.1016/j.scitotenv.2024.171484
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    Zs.A 949: Show issues Location:
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  2. Article ; Online: Whole cell kinetics of ureolysis by Sporosarcina pasteurii.

    Lauchnor, E G / Topp, D M / Parker, A E / Gerlach, R

    Journal of applied microbiology

    2015  Volume 118, Issue 6, Page(s) 1321–1332

    Abstract: Aims: Ureolysis drives microbially induced calcium carbonate precipitation (MICP). MICP models typically employ simplified urea hydrolysis kinetics that do not account for cell density, pH effect or product inhibition. Here, ureolysis rate studies with ... ...

    Abstract Aims: Ureolysis drives microbially induced calcium carbonate precipitation (MICP). MICP models typically employ simplified urea hydrolysis kinetics that do not account for cell density, pH effect or product inhibition. Here, ureolysis rate studies with whole cells of Sporosarcina pasteurii aimed to determine the relationship between ureolysis rate and concentrations of (i) urea, (ii) cells, (iii) NH4+ and (iv) pH (H(+) activity).
    Methods and results: Batch ureolysis rate experiments were performed with suspended cells of S. pasteurii and one parameter was varied in each set of experiments. A Michaelis-Menten model for urea dependence was fitted to the rate data (R(2)  = 0·95) using a nonlinear mixed effects statistical model. The resulting half-saturation coefficient, Km , was 305 mmol l(-1) and maximum rate constant, Vmax , was 200 mmol l(-1)  h(-1) . However, a first-order model with k1  = 0·35 h(-1) fit the data better (R(2)  = 0·99) for urea concentrations up to 330 mmol l(-1) . Cell concentrations in the range tested (1 × 10(7) -2 × 10(8)  CFU ml(-1) ) were linearly correlated with ureolysis rate (cell dependent Vmax' = 6·4 × 10(-9)  mmol CFU(-1)  h(-1) ).
    Conclusions: Neither pH (6-9) nor ammonium concentrations up to 0·19 mol l(-1)  had significant effects on the ureolysis rate and are not necessary in kinetic modelling of ureolysis. Thus, we conclude that first-order kinetics with respect to urea and cell concentrations are likely sufficient to describe urea hydrolysis rates at most relevant concentrations.
    Significance and impact of the study: These results can be used in simulations of ureolysis driven processes such as microbially induced mineral precipitation and they verify that under the stated conditions, a simplified first-order rate for ureolysis can be employed. The study shows that the kinetic models developed for enzyme kinetics of urease do not apply to whole cells of S. pasteurii.
    MeSH term(s) Bacterial Proteins/chemistry ; Bacterial Proteins/metabolism ; Calcium Carbonate/chemistry ; Calcium Carbonate/metabolism ; Hydrolysis ; Kinetics ; Sporosarcina/chemistry ; Sporosarcina/enzymology ; Sporosarcina/metabolism ; Urea/chemistry ; Urea/metabolism ; Urease/chemistry ; Urease/metabolism
    Chemical Substances Bacterial Proteins ; Urea (8W8T17847W) ; Urease (EC 3.5.1.5) ; Calcium Carbonate (H0G9379FGK)
    Language English
    Publishing date 2015-06
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 1358023-1
    ISSN 1365-2672 ; 1364-5072
    ISSN (online) 1365-2672
    ISSN 1364-5072
    DOI 10.1111/jam.12804
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  3. Article: Whole cell kinetics of ureolysis by Sporosarcina pasteurii

    Lauchnor, E.G / Topp, D.M / Parker, A.E / Gerlach, R

    Journal of applied microbiology. 2015 June, v. 118, no. 6

    2015  

    Abstract: AIMS: Ureolysis drives microbially induced calcium carbonate precipitation (MICP). MICP models typically employ simplified urea hydrolysis kinetics that do not account for cell density, pH effect or product inhibition. Here, ureolysis rate studies with ... ...

    Abstract AIMS: Ureolysis drives microbially induced calcium carbonate precipitation (MICP). MICP models typically employ simplified urea hydrolysis kinetics that do not account for cell density, pH effect or product inhibition. Here, ureolysis rate studies with whole cells of Sporosarcina pasteurii aimed to determine the relationship between ureolysis rate and concentrations of (i) urea, (ii) cells, (iii) NH4+ and (iv) pH (H⁺activity). METHODS AND RESULTS: Batch ureolysis rate experiments were performed with suspended cells of S. pasteurii and one parameter was varied in each set of experiments. A Michaelis–Menten model for urea dependence was fitted to the rate data (R² = 0·95) using a nonlinear mixed effects statistical model. The resulting half‐saturation coefficient, Kₘ, was 305 mmol l⁻¹and maximum rate constant, Vₘₐₓ, was 200 mmol l⁻¹ h⁻¹. However, a first‐order model with k₁ = 0·35 h⁻¹fit the data better (R² = 0·99) for urea concentrations up to 330 mmol l⁻¹. Cell concentrations in the range tested (1 × 10⁷–2 × 10⁸ CFU ml⁻¹) were linearly correlated with ureolysis rate (cell dependent Vmax′ = 6·4 × 10⁻⁹ mmol CFU⁻¹ h⁻¹). CONCLUSIONS: Neither pH (6–9) nor ammonium concentrations up to 0·19 mol l⁻¹ had significant effects on the ureolysis rate and are not necessary in kinetic modelling of ureolysis. Thus, we conclude that first‐order kinetics with respect to urea and cell concentrations are likely sufficient to describe urea hydrolysis rates at most relevant concentrations. SIGNIFICANCE AND IMPACT OF THE STUDY: These results can be used in simulations of ureolysis driven processes such as microbially induced mineral precipitation and they verify that under the stated conditions, a simplified first‐order rate for ureolysis can be employed. The study shows that the kinetic models developed for enzyme kinetics of urease do not apply to whole cells of S. pasteurii.
    Keywords Sporosarcina pasteurii ; ammonium compounds ; calcium carbonate ; enzyme kinetics ; hydrolysis ; pH ; statistical models ; urea ; urease
    Language English
    Dates of publication 2015-06
    Size p. 1321-1332.
    Publishing place Published for the Society for Applied Bacteriology by Blackwell Science
    Document type Article
    ZDB-ID 1358023-1
    ISSN 1364-5072
    ISSN 1364-5072
    DOI 10.1111/jam.12804
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    Zs.A 259: Show issues Location:
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  4. Article: Whole cell kinetics of ureolysis by Sporosarcina pasteurii

    Lauchnor, E.G. / Topp, D.M. / Parker, A.E. / Gerlach, R.

    Journal of applied microbiology

    Volume v. 118,, Issue no. 6

    Abstract: AIMS: Ureolysis drives microbially induced calcium carbonate precipitation (MICP). MICP models typically employ simplified urea hydrolysis kinetics that do not account for cell density, pH effect or product inhibition. Here, ureolysis rate studies with ... ...

    Abstract AIMS: Ureolysis drives microbially induced calcium carbonate precipitation (MICP). MICP models typically employ simplified urea hydrolysis kinetics that do not account for cell density, pH effect or product inhibition. Here, ureolysis rate studies with whole cells of Sporosarcina pasteurii aimed to determine the relationship between ureolysis rate and concentrations of (i) urea, (ii) cells, (iii) NH4+ and (iv) pH (H⁺activity). METHODS AND RESULTS: Batch ureolysis rate experiments were performed with suspended cells of S. pasteurii and one parameter was varied in each set of experiments. A Michaelis–Menten model for urea dependence was fitted to the rate data (R² = 0·95) using a nonlinear mixed effects statistical model. The resulting half‐saturation coefficient, Kₘ, was 305 mmol l⁻¹and maximum rate constant, Vₘₐₓ, was 200 mmol l⁻¹ h⁻¹. However, a first‐order model with k₁ = 0·35 h⁻¹fit the data better (R² = 0·99) for urea concentrations up to 330 mmol l⁻¹. Cell concentrations in the range tested (1 × 10⁷–2 × 10⁸ CFU ml⁻¹) were linearly correlated with ureolysis rate (cell dependent Vmax′ = 6·4 × 10⁻⁹ mmol CFU⁻¹ h⁻¹). CONCLUSIONS: Neither pH (6–9) nor ammonium concentrations up to 0·19 mol l⁻¹ had significant effects on the ureolysis rate and are not necessary in kinetic modelling of ureolysis. Thus, we conclude that first‐order kinetics with respect to urea and cell concentrations are likely sufficient to describe urea hydrolysis rates at most relevant concentrations. SIGNIFICANCE AND IMPACT OF THE STUDY: These results can be used in simulations of ureolysis driven processes such as microbially induced mineral precipitation and they verify that under the stated conditions, a simplified first‐order rate for ureolysis can be employed. The study shows that the kinetic models developed for enzyme kinetics of urease do not apply to whole cells of S. pasteurii.
    Keywords enzyme kinetics ; urea ; Sporosarcina pasteurii ; urease ; hydrolysis ; ammonium compounds ; calcium carbonate ; statistical models ; pH
    Language English
    Document type Article
    ISSN 1364-5072
    Database AGRIS - International Information System for the Agricultural Sciences and Technology

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