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Globally, ocean dumping of chemical waste is a common method of disposal and relies on the assumption that dilution, diffusion, and dispersion at ocean scales will mitigate human exposure and ecosystem impacts. In southern California, extensive dumping of agrochemical waste, particularly chlorinated hydrocarbon contaminants such as DDT, via sewage outfalls and permitted offshore barging occurred for most of the last century. This study compiled a database of existing sediment and fish DDT measurements to examine how this unique legacy of regional ocean disposal translates into the contemporary contamination of the coastal ocean. We used spatiotemporal modeling to derive continuous estimates of sediment DDT contamination and show that the spatial signature of disposal (i.e., high loadings near historic dumping sites) is highly conserved in sediments. Moreover, we demonstrate that the proximity of fish to areas of high sediment loadings explained over half of the variation in fish DDT concentrations. The relationship between sediment and fish contamination was mediated by ecological predictors (e.g., species, trophic ecology, habitat use), and the relative influence of each predictor was context-dependent, with habitat exhibiting greater importance in heavily contaminated areas. Thus, despite more than half a century since the cessation of industrial dumping in the region, local ecosystem contamination continues to mirror the spatial legacy of dumping, suggesting that sediment can serve as a robust predictor of fish contamination, and general ecological characteristics offer a predictive framework for unmeasured species or locations., Competing Interests: Competing interests statement:The authors declare no competing interest.

This article tries to develop an integrated artificial neural network (ANN) model for spatial and temporal forecasting of daily suspended sediment discharge at multiple gauging stations in Eel River watershed in northwest California. Complexity of runoff–sediment process and its variability in space and time and also lack of historical sediment data cause difficulties in spatiotemporal modeling of this process. Initially, and for comparison purpose, six single-station ANN models, which are customary in modeling sediment yield, were developed. Then an integrated ANN model for modeling multiple stations was proposed and its spatiotemporal modeling ability was examined through a cross-validation technique for a station. In this way, different multilayer perceptron neural networks were trained using Levenberg–Marquardt algorithm to estimate daily values of suspended sediment discharge. Various combinations of input and hidden layers' neurons were applied and the optimum architectures of the models were selected according to the obtained evaluation criteria in the terms of Nash–Sutcliffe efficiency coefficient, root mean-squared error, and ratio of absolute error of peak flow. To improve the model, input data were classified into two clusters by k-means clustering scheme. Afterwards, clustered data were used as inputs for two integrated models and their performances were evaluated. The proposed integrated ANN model shows reasonable performance in spatiotemporal modeling both before and after clustering. Nevertheless, clustering decreases the complexity of the model. [ABSTRACT FROM AUTHOR]