Proportional contributions to organic chemical mixture effects in groundwater and surface water.

Semi-quantitative GC-MS and LC-MS measurements of organic chemicals in groundwater and surface waters were used to assess the overall magnitude and contribution of the most important substances to calculated mixture hazard. Here we use GC-MS and LC-MS measurements taken from two separate national monitoring programs for groundwater and surface water in England, in combination with chronic species sensitivity distribution (SSD) HC50 values published by Posthuma et al. (2019, Environ. Toxicol. Chem, 38, 905-917) to calculate individual substance hazard quotients and mixture effects using a concentration addition approach. The mixture analysis indicated that, as anticipated, there was an increased hazard from the presence of a cocktail of substances at sites compared to the hazard for any single chemical. The magnitude of the difference between the hazard attributed to the most important chemical and the overall mixture effect, however, was not large. Thus, the most toxic chemical contributed ≥ 20% of the calculated mixture effect in >99% of all measured groundwater and surface water samples. On the basis of this analysis, a 5 fold assessment factor placed on the risk identified for any single chemical would offer a high degree of in cases where implementation of a full mixture analysis was not possible. This finding is consistent with previous work that has assessed chemical mixture effects within field monitoring programs and as such provides essential underpinning for future policy and management decisions on how to effectively and proportionately manage mixture risks.

Worst-case ranking of organic chemicals detected in groundwaters and surface waters in England.

The Environment Agency has been using Gas Chromatography-Mass Spectrometry (GC-MS) and Accurate-mass Quadrupole Time-of-Flight (Q-TOF) / Liquid Chromatography-Mass Spectrometry (LC-MS) target screen analysis to semi-quantitatively measure organic substances in groundwater and surface water since 2009 for GC-MS and 2014 for LC-MS. Here we use this data to generate a worst-case "risk" ranking of the detected substances. Three sets of hazard values relating to effects on aquatic organisms, namely Water Framework Directive EQSs, NORMAN Network PNECs (hereafter NORMAN PNEC) and chronic Species Sensitivity Distribution (SSD) HC50s from Posthuma et al., (2019) were used for the assessment. These hazard values were compared to the highest measured concentration for each chemical to generate a worst-case hazard quotient (HQ). Calculated HQs for each metric were ranked, averaged and multiplied by rank for detection frequency to generate an overall ordering based on HQ and occurrence. This worst-case approach was then used to generate ranking lists for GC-MS and LC-MS detected substances in groundwater and surface water. Pesticides in the top 30 overall ranked list included more legacy pesticides in groundwater and more current use actives in surface water. Specific uses were linked to some high rankings (e.g. rotenone for invasive species control). A number of industrial and plastics associated chemicals were ranked highly in the groundwater dataset, while more personal care products and pharmaceuticals were highly ranked in surface waters. Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) compounds were commonly highly ranked in both environmental compartments. The approach confirmed high rankings for some substance (e.g. selected pesticides) from previous prioritization exercises, but also identified novel substance for consideration (e.g. some PFAS compounds and pharmaceuticals). Overall our approach provided a simple approach using readily accessible data to identify substances for further and more detailed assessment.

Groundwater vulnerability: interactions of chemical and site properties.

This study brings together extensive, multi-annual groundwater monitoring datasets from the UK and Midwestern US to test the relative importance of site (e.g. land use, soil and aquifer type) and chemical factors (e.g. solubility in water) and between and within year variations in controlling groundwater contamination by pesticides. ANOVA (general linear modelling) was used to test the significance and proportion of variation explained by each factor and their interactions. Results from both the UK and US datasets show that: (i) Chemical and site factors both have a statistically significant influence on groundwater pollution; (ii) Site factors on their own explain a greater proportion of data variance than chemical factors on their own; (iii) Interaction between site and chemical factors represents the most important control on the occurrence of pesticides in groundwater; (iv) Variation within the year was slight but still significant while there was no significant difference between data from consecutive years. The combination of factors analysed in this study were sufficient to explain the majority of the variation in the data save for that ascribable to the analytical detection limit. The results provide statistical evidence that it is viable to develop both molecular methods and groundwater vulnerability as tools to understanding pollution, but that a greater emphasis should be placed on their interaction to fully understand pesticide contamination.