Strategies for including vegetation compartments in multimedia models

The incentives for including vegetation compartments in multimedia Level I, II and III fugacity calculations are discussed and equations and parameters for undertaking the calculations suggested. Model outputs with and without vegetation compartments are compared for 12 non-ionic organic chemicals with a wide variety of physical-chemical properties. Inclusion of vegetation compartments is shown to have a significant effect on two classes of chemicals: (1) those that are taken up by atmospheric deposition and (2) those that are taken up by transpiration through the plant roots. It is suggested that uptake from the atmosphere is important for chemicals with logK(OA) greater than 6 and a logK(AW) of greater than -6. Plant uptake by transpiration is important for chemicals with logK(OW) less than 2.5 and a logK(AW) of less than -1. At logK(OA) > 9 atmospheric uptake is dominated by particle-bound deposition and the importance of partitioning to vegetation is largely dependent on the relative magnitude of the particle deposition velocities to soil and vegetation. These property ranges can be used to determine if a chemical will significantly partition to vegetation. If the chemical falls outside the property ranges of the two classes it will probably be unnecessary to include vegetation in models for assessing environmental fate. The amount of chemical predicted to partition to vegetation compartments in the model is shown to be highly sensitive to certain model assumptions. Further experimental research is recommended to obtain more reliable equations describing equilibrium partitioning and uptake/depuration kinetics.     

A noval method of predicting soil organic carbon content normalized adsorption coefficients (Koc) of polybrominated biphenyls (PBBs)

By a soil column liquid chromatograph (SCLC) method, the soil organic carbon content normalized adsorption coefficients (K(oc)) of six polybrominated biphenyls (PBB15, PBB26, PBB31, PBB49, PBB103 and PBB153) are determined. Based on the similarity between the molecular structures of PBBs and PCBs, a simple linear predictive model has been developed with the correlation coefficient R=0.9812 and standard error SE=0.19. The logK(oc) values of any PBB congeners can be predicted by using the logK(oc) values of the corresponding PCBs according to this model. Using the published data for logK(oc) values of PCB congeners, logK(oc) values of all 209 PBB congeners have been for the first time predicted. Compared with the data obtained from the experiment, the results of prediction are very accurate.