Sources of uncertainty in pesticide fate modelling

Dubus, Igor G.; Brown, Colin D.; Beulke, Sabine

doi:10.1016/s0048-9697(03)00362-0

Cited by 170 publications

(111 citation statements)

References 135 publications

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“…e R 2 adj coe cients obtained with the rank-transformed data varied broadly depending on the outputs and herbicides considered re ecting the inherent nonlinear character of the relationship between inputs and outputs found in general pesticide fate models. 9,44) e R 2 adj were, however higher than 0.7; therefore, the SRRC can be considered as a valid measure of sensitivity. 58) Only the positive standardized regression coe cients evaluated by the t-test were used as a measure of sensitivity in the analysis.…”

Section: Reliability Of Uncertainty and Sensitivity Analysesmentioning

confidence: 94%

“…For the soil/water partitioning coe cient of pesticide (k d-PSL ), the parameter in previous studies was assumed to have either a normal, a uniform or a log-normal distribution. 9) Since no robust distribution could be selected, the k d-PSL values were derived on the basis of xed values of the soil organic carbon sorption coe cient (k OC ) and the distribution of organic carbon (O C ). [38][39][40] e choice of the shapes and types of distributions re ects the magnitude, range, and interpretation of each parameter.…”

Section: Monte Carlo Simulation and Parameter Selectionmentioning

confidence: 99%

“…9,44) A uniform distribution was assigned to the saturated volumetric water content, as the shape of the distribution was reported to be unknown. 10) e Crystal Ball ® so ware 45) was used as an interface with the PCPF-1 model for conducting Monte Carlo simulation; we refer to this model system as PCPF-MCS hereina er.…”

Section: Monte Carlo Simulation and Parameter Selectionmentioning

confidence: 99%

“…In general, modeling uncertainty was shown to lead to a lack of con dence of the model performance. 9,10) e uncertainty analysis is hence essential to provide assistance to users for model parameterization, calibration, and probabilistic modeling. [11][12][13][14] In the context of hydrologic models, general uncertainty was classi ed into three types [15][16][17] : the inherent variability in natural processes, model uncertainty, and parameter uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

Boulangé

Kondo

Thai

et al. 2012

Journal of Pesticide Science

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is paper demonstrates the procedures for probabilistic assessment of a pesticide fate and transport model, PCPF-1, to elucidate the modeling uncertainty using the Monte Carlo technique. Sensitivity analyses are performed to investigate the in uence of herbicide characteristics and related soil properties on model outputs using four popular rice herbicides: mefenacet, pretilachlor, bensulfuron-methyl and imazosulfuron. Uncertainty quanti cation showed that the simulated concentrations in paddy water varied more than those of paddy soil. is tendency decreased as the simulation proceeded to a later period but remained important for herbicides having either high solubility or a high 1st-order dissolution rate. e sensitivity analysis indicated that PCPF-1 parameters requiring careful determination are primarily those involve with herbicide adsorption (the organic carbon content, the bulk density and the volumetric saturated water content), secondary parameters related with herbicide mass distribution between paddy water and soil (1st-order desorption and dissolution rates) and lastly, those involving herbicide degradations.

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Section: Reliability Of Uncertainty and Sensitivity Analysesmentioning

confidence: 94%

Section: Monte Carlo Simulation and Parameter Selectionmentioning

confidence: 99%

Section: Monte Carlo Simulation and Parameter Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

Boulangé

Kondo

Thai

et al. 2012

Journal of Pesticide Science

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“…Therefore, it is prudent to use the biodegradation in surface water as higher-tier site-/ season-specific data, since its variability is considered much higher than that in aerobic soil. 175) Microbes freely floating in water represent a part of all microbes, and those associated with suspended sediments also play a role in biodegradation of pesticides. Therefore, the effect of sediment should be examined together with its possible sink for pesticides due to adsorption, as prescribed in the guidelines.…”

Section: Resultsmentioning

confidence: 99%

Aerobic microbial transformation of pesticides in surface water

Katagi

2013

J. Pestic. Sci.

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Aerobic transformation by microbial organisms is a dissipation process of pesticides in surface water, but the corresponding information is much less available as compared with their microbial degradation in soil. Bacteria freely floating in water or associated with suspended particles are the key microorganisms degrading pesticides; their species and populations, however, depend on sites and seasons. The various factors related to pesticide properties, experimental conditions and characteristics of surface water are involved in the complex control of microbial processes. Bottom sediment and macrophytes with associated biofilms not only act as sink for pesticides but also provide the habitat for both bacteria and fungi that degrade pesticides. More understanding of each factor is necessary to utilize laboratory biodegradation data for the refined assessment of pesticide behavior in surface water.

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Closed‐flow column experiments: A numerical sensitivity analysis of reactive transport and parameter uncertainty

Ritschel

Totsche

2016

Water Resources Research

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The identification of transport parameters by inverse modeling often suffers from equifinality or parameter correlation when models are fitted to measurements of the solute breakthrough in column outflow experiments. This parameter uncertainty can be approached by performing multiple experiments with different sets of boundary conditions, each provoking observations that are uniquely attributable to the respective transport processes. A promising approach to further increase the information potential of the experimental outcome is the closed-flow column design. It is characterized by the recirculation of the column effluent into the solution supply vessel that feeds the inflow, which results in a damped sinusoidal oscillation in the breakthrough curve. In order to reveal the potential application of closed-flow experiments, we present a comprehensive sensitivity analysis using common models for adsorption and degradation. We show that the sensitivity of inverse parameter determination with respect to the apparent dispersion can be controlled by the experimenter. For optimal settings, a decrease in parameter uncertainty as compared to classical experiments by an order of magnitude is achieved. In addition, we show a reduced equifinality between rate-limited interactions and apparent dispersion. Furthermore, we illustrate the expected breakthrough curve for equilibrium and nonequilibrium adsorption, the latter showing strong similarities to the behavior found for completely mixed batch reactor experiments. Finally, breakthrough data from a reactive tracer experiment is evaluated using the proposed framework with excellent agreement of model and experimental results.

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Sources of uncertainty in pesticide fate modelling

Cited by 170 publications

References 135 publications

Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

Aerobic microbial transformation of pesticides in surface water

Closed‐flow column experiments: A numerical sensitivity analysis of reactive transport and parameter uncertainty

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