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A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.

This paper presents and evaluates an inverse model for estimating ammonia emission from agricultural land. The method is based on an analytical model derived from the advection-diffusion equation, assuming power law profiles for wind speed and diffusivity. A three-dimensional model and a two-dimensional model are evaluated. The hypotheses of flux-driven or concentration-driven emissions are also tested. The model is evaluated against three datasets covering a range of ammonia fluxes, field geometry/size and measurement techniques. The sensitivity and the uncertainty of the method is also evaluated with a MonteCarlo approach, as well as based on existing datasets. Finally, the capability of the method to work with time-integrated concentrations (e.g. using diffusive concentration samplers) is also evaluated. The inverse model gives estimations of the ammonia emissions within a few per cent of the measurements. Moreover, the method is mainly sensitive to the concentration, the friction velocity and the thermal stratification of the atmosphere. The two-dimensional approaches give similar results to the three-dimensional one, provided the field is large enough. The concentration-driven hypothesis is similar to the flux-driven hypothesis for a fetch greater than approximately 20 m. The results are discussed in comparison with the previous approaches: the Theoretical Profile Shape (TPS or Zinst approach) and the backward Lagrangian Stochastic model (BLS).

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