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

TY - JOUR

T1 - Temporal modelling of long-term heavy metal concentrations in aquatic ecosystems

AU - Bushra, Basmah

AU - Bazneh, Leyla

AU - Deka, Lipika

AU - Wood, Paul J.

AU - McGowan, Suzanne

AU - Das, Diganta B.

N1 - Data archiving: no NIOO data

PY - 2023/7/1

Y1 - 2023/7/1

N2 - 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.

AB - 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.

KW - aquatic ecosystems

KW - artificial neural network

KW - metal concentration

KW - multiple regression analysis

KW - time series analysis

U2 - 10.2166/hydro.2023.151

DO - 10.2166/hydro.2023.151

M3 - Article

AN - SCOPUS:85168126835

SN - 1464-7141

VL - 25

SP - 1188

EP - 1209

JO - Journal of Hydroinformatics

JF - Journal of Hydroinformatics

IS - 4

ER -