Objectives:The goal of the proposed work is to develop adaptive sampling strategies for employing low-cost, high-performance autonomous underwater vehicles (AUVs) for monitoring waste water and dredged material discharge. Such techniques have the potential to dramatically increase the efficiency of economy of survey operations, both of AUVs and of other survey platforms as well.Methodology:Work will consist of two components: first, sophisticated simulation tools will be employed for testing adaptive sampling strategies; and second, key experiments will be carried out with existing Odyssey II autonomous underwater vehicles in the field. To provide a realistic simulation environment, this existing AUV simulator will be merged with one of the numerical transport models developed in the Parsons Laboratory, e.g. the TEA/ELA model currently being used for Boston Harbor CSO simulation. Both simulation and field operation will focus on Boston Harbor and Massachusetts Bay, locations chosen because they have been the focus of extensive experimental and theoretical studies in conjunction with the Boston Harbor Clean Up Program. Rationale:To obtain synoptic data, a survey system must be capable of mapping an ocean structure faster than significant changes occur in that structure. Except for satellite imagery of the ocean surface, very few oceanographic measurement programs meet this goal, especially in the dynamic coastal environment. The fundamental idea underlying adaptive sampling is to increase survey efficiency by concentrating measurements in regions of interest. Thus, to map a waste water plume, one might first run a very coarse survey to localize the plume, then concentrate operations in the plume vicinity. Substantial savings can be realized both of expended energy and time required to characterize the plume.