Historically, pristine sources of water are increasingly threatened by contamination from a plethora of point and non-point discharges. With over 15,000 new chemicals introduced daily, it is impractical to monitor each one. Non-targeted methods using HRMScan identify many compounds, but the contents of these discharges are not often available in databases. Determination of what components are significant and identifying them is critical, but difficult given the variability of environmental samples across time and season. This work examines LC/HRMS data of a river influenced by agriculture, hydraulic fracturing and domestic wastewater from January to November 2016 using multivariate statistics to determine compounds that are consistently being discharged into the environment and should be identified and monitored. Replicate samples of wastewater effluent, downstream, tributaries, field blanks and lab blanks were analyzed. Figure 1 shows a PCA of a sample set taken in February of LC/QTOF MS feature finding results. The effluent (#7) of a wastewater treatment plant is well separated from the other samples and indicates that anthropogenic contaminants can be distinguished from naturally occurring compounds. Non-targeted analysis using acustom database (1200 compounds) gave positive identification of carbamazepine, diphenhydramine, enrofloxacin, fexofenadine and others in the samples. The non-targeted database analysis also demonstrates that although compounds of interest can be determined, there are many more compounds that cannot using a limited database. With the locations sampled across several months, statistical techniques are used to distinguish unknowns not in a database that appear consistently and thus should be identified to determine their importance as an emerging contaminant. QA/QC spikes were used to assure the appropriate use of both data processing features and statistical methods.

Identification of emerging contaminants from the waste water influenced Tioga River using high resolution accurate mass LC/MS and statistical analysis

253rd National Meeting (2017)

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