Automated high-temporal-resolution multi-element river water monitoring with ICP-MS - Mohamed Harhash; Jan G. Wiederhold; Ingrid Nett; Julia Arndt; Alexander Zavarsky; Thomas A. Ternes; Lars Duester (Environmental Sciences Europe)
Abstract Background Real-time monitoring of multi-element concentrations in rivers is highly desirable due to potential hazardous impacts and a better understanding of geochemical cycles. Most of the available datasets are generated from low-frequency sampling (weekly or monthly), include only a limited number of elements, and fail to capture rapid changes and short-term events occurring between two sampling dates. Relying on such sparse data limits our understanding of the behavior of elements and their temporal changes within catchments. In consequence, this leads to inaccuracies in mass balance calculations and a loss of awareness for sudden pollution events. Thus, there is an urgent need for continuous fully quantitative high-temporal-resolution multi-element monitoring of river water to provide comprehensive datasets to improve water management, also with respect to machine learning training and prognosis. Results Therefore, we developed an automated atline method using inductively coupled plasma-mass spectrometry (ICP-MS) to continuously measure 56 elements in river water at a high time resolution of one mixed sample per hour. To achieve this, we constructed a self-cleaning fraction Collector/AuTosampler system (CAT) which continuously collects and provides samples (24/7) to the ICP-MS. The CAT was coupled with an ICP-QMS and an ICP-QQQ-MS which were, for the first time, fully automated and fully quantitative operated in river water monitoring using a Python script. Our results provided hourly concentrations of 56 elements in the Rhine River (Germany) for a period of 1 month and allowed to investigate short-term events such as high river discharge and their impacts on element concentrations. The time series of continuously measured electrical conductivity and the major elements indicated a similar variability. The results also highlight potential inaccuracies caused by using only low-frequency sampling that can accumulate in annual estimates. Conclusions The fully automated atline high-resolution monitoring approach is delivered completely and transparently in this study and represents a breakthrough in element monitoring automation, offering a valuable tool to understand complex element behaviors and to detect rapid changes in river water in close to real time.