Temporal modelling of long-term heavy metal concentrations in aquatic ecosystems

Abstract

This paper examines a series of connected and isolated lakes in the UK as a model system with historic episodes of heavy metal contamination. A 9-year hydrometeorological dataset for the sites was identified to analyse the legacy of heavy metal concentrations within the selected lakes based on physico-chemical and hydrometeorological parameters, and a comparison of the complementary methods of multiple regression, time series analysis, and artificial neural network (ANN). The results highlight the importance of the quality of historic datasets without which analyses such as those presented in this research paper cannot be undertaken. The results also indicate that the ANNs developed were more realistic than the other methodologies (regression and time series analysis) considered. The ANNs provided a higher correlation coefficient and a lower mean squared error when compared to the regression models. However, quality assurance and pre-processing of the data were challenging and were addressed by transforming the relevant dataset and interpolating the missing values. The selection and application of the most appropriate temporal modelling technique, which relies on the quality of available dataset, is crucial for the management of legacy contaminated sites to guide successful mitigation measures to avoid significant environmental and human health implications. HIGHLIGHTS Heavy metal contamination in aquatic ecosystems is a significant environmental concern.; A series of connected and isolated lakes were examined as a model system.; A 9-year hydrological and meteorological dataset was analysed.; Multiple regression, time series analysis, and artificial neural networks were used.; The accuracy of ANN models was better than the other methods tested.;

Keywords

• aquatic ecosystems
• artificial neural network
• metal concentration
• multiple regression analysis
• time series analysis