Exposure assessment of 17alpha-ethinylestradiol in surface waters of the United States and Europe.

An evaluation of measured and predicted concentrations of 17-ethinylestradiol in surface waters of the United States and Europe was conducted to develop expected long-term exposure concentrations for this compound. Measured environmental concentrations (MECs) in surface waters were identified from the literature. Predicted environmental concentrations (PECs) were generated for European and U.S. watersheds using the GREAT-ER and PhATE models, respectively. The majority of MECs are nondetect and generally consistent with model PECs and conservative mass balance calculations. However, the highest MECs are not consistent with concentrations derived from conservative (worst-case) mass balance estimates or model PECs. A review of analytical methods suggests that tandem or high-resolution mass spectrometry methods with extract cleanup result in lower detection limits and lower reported concentrations consistent with model predictions and bounding estimates. Based on model results using PhATE and GREAT-ER, the 90th-percentile low-flow PECs in surface water are approximately 0.2 and 0.3 ng/L for the United States and Europe, respectively. These levels represent conservative estimates of long-term exposure that can be used for risk assessment purposes. Our analysis also indicates that average concentrations are one to two orders of magnitude lower than these 90th-percentile estimates. Higher reported concentrations (e.g., greater than the 99th-percentile PEC of approximately 1 ng/L) could result from methodological problems or unusual environmental circumstances; however, such concentrations are not representative of levels generally found in the environment, warrant special scrutiny, and are not appropriate for use in risk assessments of long-term exposures.

Screening analysis of human pharmaceutical compounds in U.S. surface waters.

The PhATE (Pharmaceutical Assessment and Transport Evaluation) model presented in this paper was developed as a tool to estimate concentrations of active pharmaceutical ingredients (APIs) in U.S. surface waters that result from patient use (or consumption) of medicines. PhATE uses a mass balance approach to model predicted environmental concentrations (PECs) in 11 watersheds selected to be representative of most hydrologic regions of the United States. The model divides rivers into discrete segments. It estimates the mass of API that enters a segment from upstream or from publicly owned treatment works (POTW) and is subsequently lost from the segment via in-stream loss mechanisms or flow diversions (i.e., man-made withdrawals). POTW discharge loads are estimated based on the population served, the API use per capita, the potential loss of the compound associated with human use (e.g., metabolism), and the portion of the API mass removed in the POTW. Simulations using three surrogate compounds showthat PECs generated by PhATE are generally within an order of magnitude of measured concentrations and that the cumulative probability distribution of PECs for all watersheds included in PhATE is consistent with the nationwide distribution of measured concentrations of the surrogate compounds. Model simulations for 11 APIs yielded four categories of results. (1) PECs fit measured data for two compounds. (2) PECs are below analytical method detection limits and thus are consistent with measured data for three compounds. (3) PECs are higher than (i.e., not consistent with) measured data for three compounds. However, this may be the consequence of as yet unidentified depletion mechanisms. (4) PECs are several orders of magnitude below some measured data but consistentwith most measured data forthree compounds. For the fourth category, closer examination of sampling locations suggests that the field-measured concentrations for these compounds do not accurately reflect human use. Overall, these results demonstrate that PhATE may be used to predict screening-level concentrations of APIs and related compounds in the environment as well as to evaluate the suitability of existing fate information for an API.