Automated pretreatment of environmental water samples and non-targeted intelligent screening of organic compounds based on machine experiments

Abstract

The complexity of environmental pollutants poses significant challenges for monitoring and analysis, especially with the emergence of numerous emerging contaminants. Traditional analysis methods rely mainly on laboratory analysis, which involves labor-intensive and time-consuming sample preparation procedures and non-target data analysis, greatly limiting the rapid detection of water organic pollutants. In this study, we designed a robot experimenter combined with GC × GC-TOFMS. By configuring self-developed automated analysis software, we established a fully automated process from sample collection to data characterization, for the analysis of organic pollutants. We validated the method with 111 organic standards compounds. The robot performed 2577 actions covering the entire workflow, from water sample collection to sample pre-treatment. The integration of mass spectrometry and related software enabled the automatic analysis of emerging hazardous contaminants, from sampling to the output of detection results. The results showed the automated process could qualitatively identify all compounds and demonstrated good linearity, low detection limits, and excellent quantitative ability within the range of 0.04–0.4 mg/L. The average recoveries of 82.89 % of the samples ranged from 70 % to 120 % (relative standard deviation (RSD) <15 %) at different spiked concentrations. This indicated that the established method could be used for non-targeted analysis of emerging contaminants in environmental water samples. We applied the method to samples from wastewater treatment plants and river sections, identifying 1,902 compounds across 26 categories, including 6 known hazardous contaminants found in all samples. The relative content of these characteristic compounds will inform whether treated wastewater meets discharge standards and aid in tracing the sources of pollutants. Therefore, the development of this fully automated machine experimental method enables real-time and online automatic analysis of organic pollutants in environmental water. The establishment of characteristic fingerprints can provide technical support for early warning and traceability of water quality.