Polyparameter linear free energy relationships (PP-LFERs) have increasingly been used to predict equilibrium partition coefficients relevant for environmental behavior of organic chemicals because of their high accuracy, robustness, and mechanistic basis. PP-LFERs are multiple linear regression models that use several molecular descriptors as independent variables. The descriptors capture the free energy contributions of all relevant molecular interactions. Hence, a wide application domain can be covered with a relatively limited calibration effort. PP-LFERs can typically achieve an accuracy of rmse=0.2-0.3 log units in homogeneous partition systems, being more accurate than other frequently used QSARs including log K_ow correlations. In this contribution, we review the recent advancement on PP-LFER applications for evaluation and prediction of environmental partition coefficients and identify research gaps. To predict a partition coefficient, (i) PP-LFER regression coefficients (or system parameters) specific to the given partitioning system and (ii) compound descriptors for the compound of interest are needed. In the past decade, PP-LFER system parameters have been calibrated for partitioning into many important environmental matrices such as soil organic matter, humic substances, aerosol, and mineral surfaces. Biological phases such as lipids and proteins have recently been characterized, allowing extension of PP-LFERs to biopartitioning. Considerable efforts have been made to determine PP-LFERs for carbonaceous sorbents (e.g., activated carbons) and technical polymers (e.g., PDMS, polyurethane) that are useful for environmental remediation and analysis. Compound descriptors have been measured for a number of environmentally relevant chemicals, e.g., POPs chemicals, flame retardants, organosiloxanes, perfluorinated compounds (PFCs), hormones, and pesticides. There is an on-going project to compile the compound descriptors published so far in the literature to render the descriptors more accessible. The topics that need further investigation include: determination of descriptors of super-hydrophobic compounds, recalibration of system parameters for siloxanes and PFCs, and a priori predictions of compound descriptors from molecular structure.