Dilution factors are a critical component in estimating concentrations of so‐called “down‐the‐drain” chemicals (e.g., pharmaceuticals) in rivers. The present study estimated the temporal and spatial variability of dilution factors around the world using geographically referenced data sets at 0.5° × 0.5° resolution. Domestic wastewater effluents were derived from national per capita domestic water use estimates and gridded population. Monthly and annual river flows were estimated by accumulating runoff estimates using topographically derived flow directions. National statistics, including the median and interquartile range, were generated to quantify dilution factors. Spatial variability of the dilution factor was found to be considerable; for example, there are 4 orders of magnitude in annual median dilution factor between Canada and Morocco. Temporal variability within a country can also be substantial; in India, there are up to 9 orders of magnitude between median monthly dilution factors. These national statistics provide a global picture of the temporal and spatial variability of dilution factors and, hence, of the potential exposure to down‐the‐drain chemicals. The present methodology has potential for a wide international community (including decision makers and pharmaceutical companies) to assess relative exposure to down‐the‐drain chemicals released by human pollution in rivers and, thus, target areas of potentially high risk. Environ Toxicol Chem 2014;33:447–452. © 2013 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial, and no modifications or adaptations are made.

Nano silver and nano zinc-oxide monthly concentrations in surface waters across Europe were modeled at ~6 x 9 km spatial resolution. Nano-particle loadings from households to rivers were simulated considering household connectivity to sewerage, sewage treatment efficiency, the spatial distribution of sewage treatment plants, and their associated populations. These loadings were used to model temporally varying nano-particle concentrations in rivers, lakes and wetlands by considering dilution, downstream transport, water evaporation, water abstraction, and nano-particle sedimentation. Temporal variability in concentrations caused by weather variation was simulated using monthly weather data for a representative 31-year period. Modeled concentrations represent current levels of nano-particle production.Two scenarios were modeled. In the most likely scenario, half the river stretches had long-term average concentrations exceeding 0.002 ng L(-1) nano silver and 1.5 ng L(-1) nano zinc oxide. In 10% of the river stretches, these concentrations exceeded 0.18 ng L(-1) and 150 ng L(-1), respectively. Predicted concentrations were usually highest in July.

The Centre for Ecology & Hydrology – Gridded Estimates of Areal Rainfall (CEH-GEAR) data set was developed to provide reliable 1 km gridded estimates of daily and monthly rainfall for Great Britain (GB) and Northern Ireland (NI) (together with approximately 3500 km² of catchment in the Republic of Ireland) from 1890 onwards. The data set was primarily required to support hydrological modelling. The rainfall estimates are derived from the Met Office collated historical weather observations for the UK which include a national database of rain gauge observations. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall (AAR), was used to generate the daily and monthly rainfall grids. To derive the monthly estimates, rainfall totals from monthly and daily (when complete month available) rain gauges were used in order to obtain maximum information from the rain gauge network. The daily grids were adjusted so that the monthly grids are fully consistent with the daily grids. The CEH-GEAR data set was developed according to the guidance provided by the British Standards Institution. The CEH-GEAR data set contains 1 km grids of daily and monthly rainfall estimates for GB and NI for the period 1890–2012. For each day and month, CEH-GEAR includes a secondary grid of distance to the nearest operational rain gauge. This may be used as an indicator of the quality of the estimates. When this distance is greater than 100 km, the estimates are not calculated due to high uncertainty. CEH-GEAR is available from doi:10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e and is free of charge for commercial and non-commercial use subject to licensing terms and conditions.

The occurrence of intersex fish is widespread in the rivers of England and Wales. The extent of intersex in fish populations is believed to be strongly linked to their exposure to steroid estrogens. The present study presents, to our knowledge, the first national, catchment-based risk assessment for steroid estrogens in the world. A graphical information system–based model predicted the concentrations of estradiol (E2), estrone, and ethinylestradiol, which were combined and compared with known biological effect levels to predict the risk of endocrine disruption for 10,313 individual river reaches (21,452 km) receiving effluent from more than 2,000 sewage treatment plants serving more than 29 million people. The large scale of this assessment underlines the usefulness of computer-based risk assessment methods. Overall, 39% of the modeled reaches (all percentages are in terms of the total river length modeled) in England and Wales were predicted to be not at risk from endocrine disruption (mean concentrations, 10 ng/L E2 equivalents). Many of these high-risk reaches, however, were ditches, which were composed almost entirely of sewage effluent. The model could be applied equally well to any other chemical of concern emanating from the human population that would be impractical to assess by measurement.

In order to establish the environmental impact of an active pharmaceutical ingredient (API), good information on the level of exposure in surface waters is needed. Exposure concentrations are typically estimated using information on the usage of an API as well as removal rates in the patient, the wastewater system and in surface waters. These input data are often highly variable and difficult to obtain, so model estimates often do not agree with measurements made in the field. In this paper we present an approach which uses inverse modelling to estimate overall removal rates of pharmaceuticals at the catchment scale using a hydrological model as well as prescription and monitoring data for a few representative sites for a country or region. These overall removal rates are then used to model exposure across the broader landscape. Evaluation of this approach for APIs in surface waters across England and Wales showed good agreement between modelled exposure distributions and available monitoring data. The use of the approach, alongside estimates of predicted no-effect concentrations for the 12 study compounds, to assess risk of the APIs across the UK landscape, indicated that, for most of the compounds, risks to aquatic life were low. However, ibuprofen was predicted to pose an unacceptable risk in 49.5% of the river reaches studied. For diclofenac, predicted exposure concentrations were also compared to the Environmental Quality Standard previously proposed by the European Commission and 4.5% of river reaches were predicted to exceed this concentration. While the current study focused on pharmaceuticals, the approach could also be valuable in assessing the risks of other 'down the drain' chemicals and could help inform our understanding of the important dissipation processes for pharmaceuticals in the pathway from the patient to ecological receptors.