Based on the structure characteristics of bromine substituents and conjugated matrix

a new path location index mP is defined. The electrotopological state indices (E9) and electronegativity distance vector (M26) of 209 polybrominated diphenyl ethers (PBDEs) are calculated. It shows that mP

E9 and M26 have good structural selectivity for organic pollutant. Then

the multiple regression method is used to establish relationships between the molecular structure of PBDEs and four parameters: gas-chromatographic relative retention time (tR)

n-octanol pair partition coefficient (lgKOA)

supercooled liquid vapour pressures (lgPL) and toxicity (lgI). With the four predictive models

the correlation coefficients are 0.991

0.997

0.998 and 0.904 with the standard deviation of 0.031

0.104

0.100 and 0.358

respectively. Three structural parameters are used as the input neurons of the BP neural network and different network architectures to get several satisfying neural network models for predicting tR

lgKOA

and lgPL. The results are better than those by the multiple regression method

which shows significant nonlinear relationships between the physicochemical properties of PBDEs and the three structural parameters.