title: Meta-modeling of the pesticide fate model MACRO for groundwater exposure assessments using artificial neural networks.
abstract: Several simple index methods that use easily accessible data have been developed and included in decision-support systems to estimate pesticide leaching across larger areas. However, these methods often lack important process descriptions (e.g. macropore flow), which brings into question their reliability. Descriptions of macropore flow have been included in simulation models, but these are too complex and demanding for spatial applications. To resolve this dilemma, a neural network simulation meta-model of the dual-permeability macropore flow model MACRO was created for pesticide groundwater exposure assessment. The model was parameterized using pedotransfer functions that require as input the clay and sand content of the topsoil and subsoil, and the topsoil organic carbon content. The meta-model also requires the topsoil pesticide half-life and the soil organic carbon sorption coefficient as input. A fully connected feed-forward multilayer perceptron classification network with two hidden layers, linked to fully connected feed-forward multilayer perceptron neural networks with one hidden layer, trained on sub-sets of the target variable, was shown to be a suitable meta-model for the intended purpose. A Fourier amplitude sensitivity test showed that the model output (the 80th percentile average yearly pesticide concentration at 1 m depth for a 20 year simulation period) was sensitive to all input parameters. The two input parameters related to pesticide characteristics (i.e. soil organic carbon sorption coefficient and topsoil pesticide half-life) were the most influential, but texture in the topsoil was also quite important since it was assumed to control the mass exchange coefficient that regulates the strength of macropore flow. This is in contrast to models based on the advection-dispersion equation where soil texture is relatively unimportant. The use of the meta-model is exemplified with a case-study where the spatial variability of pesticide leaching is mapped for a small field. It was shown that the area of the field that contributes most to leaching depends on the properties of the compound in question. It is concluded that the simulation meta-model of MACRO should prove useful for mapping relative pesticide leaching risks at large scales.
authors.affiliation: Department of Soil Sciences, Swedish University of Agricultural Sciences, P.O. Box 7014, 750 07 Uppsala, Sweden. Fredrik.Stenemo@mv.slu.se
authors.forename: Fredrik
authors.initials: F
authors.lastname: Stenemo
authors.forename: Anna M L
authors.initials: AM
authors.lastname: Lindahl
authors.forename: Annemieke
authors.initials: A
authors.lastname: Gärdenäs
authors.forename: Nicholas
authors.initials: N
authors.lastname: Jarvis
mesh_terms: D000327:Adsorption; D000538:Aluminum Silicates; D002244:Carbon / Q000737:chemistry; D000077215:Clay; D003198:Computer Simulation; D004784:Environmental Monitoring / Q000379:methods*; D007700:Kinetics; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010573:Pesticide Residues / Q000032:analysis; D010575:Pesticides / Q000032:analysis*; D012822:Silicon Dioxide; D012984:Software; D014874:Water Pollutants, Chemical / Q000032:analysis; D018508:Water Purification / Q000379:methods*
chemical_list: D000538:Aluminum Silicates; D010573:Pesticide Residues; D010575:Pesticides; D014874:Water Pollutants, Chemical; D002244:Carbon; D012822:Silicon Dioxide; D000077215:Clay