Quantitative interpretation and prediction of Hofmeister ion effects on protein processes, including folding and crystallization, have been elusive goals of a century of research. Here, a quantitative thermodynamic analysis, developed to treat noncoulombic interactions of solutes with biopolymer surface and recently extended to analyze the effects of Hofmeister salts on the surface tension of water, is applied to literature solubility data for small hydrocarbons and model peptides. This analysis allows us to obtain a minimum estimate of the hydration b(1) (H2O angstrom(-2)), of hydrocarbon surface and partition coefficients K, characterizing the distribution of salts and salt ions between this hydration water and bulk water. Assuming that Na+ and SO42- ions of Na2SO4 (the salt giving the largest reduction in hydrocarbon solubility as well as the largest increase in surface tension) are fully excluded from the hydration water at hydrocarbon surface, we obtain the same b(1) as for air-water surface (similar to 0.18 H2O angstrom). Rank orders of cation and anion partition coefficients for nonpolar surface follow the Hofmeister series for protein processes, but are strongly offset for cations in the direction of exclusion (preferential hydration). By applying a coarse-grained decomposition of water accessible surface area (ASA) into nonpolar, polar amide, and other polar surface and the same hydration b(1) to interpret peptide solubility increments, we determine salt partition coefficients for amide surface. These partition coefficients are separated into single-ion contributions based on the observation that both Cl- and Na+ (also K+) occupy neutral positions in the middle of the anion and cation Hofmeister series for protein folding. Independent of this assignment, we find that all cations investigated are strongly accumulated at amide surface while most anions are excluded. Cation and anion effects are independent and additive, allowing successful prediction of Hofmeister salt effects on micelle formation and other processes from structural information (ASA).