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A Model Describing Pesticide Bioavailability and Biodegradation in Soil
Author(s) -
Shelton Daniel R.,
Doherty Michael A.
Publication year - 1997
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj1997.03615995006100040013x
Subject(s) - sorption , biodegradation , bioavailability , organic matter , soil water , environmental chemistry , bacterial growth , nonlinear regression , pesticide , chemistry , diffusion , soil science , biomass (ecology) , environmental science , mathematics , thermodynamics , regression analysis , bacteria , ecology , organic chemistry , biology , adsorption , bioinformatics , genetics , statistics , physics
A model is proposed for describing pesticide‐substrate bioavailability and rates of biodegradation in soil. The model accounts for sorption to soil surfaces, diffusion into the internal matrix of soil organic matter or aggregates, and microbial growth. Rates of sorption and diffusion are approximated by first‐order kinetics while microbial growth is approximated by Monod kinetics. Model verification was performed using 2,4‐D (2,4‐dichlorophenoxyacetic acid) degradation data from high and low organic matter soils inoculated with pure cultures of 2,4‐D degrading bacteria. Estimates of sorption, diffusion, and Monod constants were obtained sequentially by fitting the defining differential equations to the data using nonlinear regression techniques. Independent estimates of initial biomass ( X 0 ) and growth yield ( Y ) were required, although X 0 could be approximated from the number of colony‐forming units assuming a bacterial weight of 0.1 pg cell −1 . The model could account for the partitioning of 2,4‐D between soluble and sorbed phases and provided estimates of the Monod constants, µ max and K S , which were generally consistent with values previously determined in pure culture. In conjunction with pest toxicology data, the model may be useful in predicting the time between application and loss of efficacy (i.e., window of efficacy) for biodegradable pesticides.