Ecotoxicity threshold values for 4-hydroxychlorothalonil, carbendazim, dimethoate and methoxyfenozide in fresh and marine waters: Part 1. Derivation of threshold values.

Pesticide active ingredients are frequently detected in the rivers, creeks, wetlands, estuaries, and marine waters of the Great Barrier Reef (GBR) region and are one of the main contributors to poor water quality. Pesticide concentrations detected in the environment through water quality monitoring programs can be compared against estimates of ecologically "safe" concentrations (i.e., water quality guidelines) to assess the potential hazard and risk posed to aquatic ecosystems. Water quality guidelines are also required to estimate the aquatic risk posed by pesticide mixtures, which is used for the Reef 2050 Water Quality Improvement Plan pesticide target. Seventy-four pesticide active ingredients and their degradates are frequently detected in GBR catchment waterways, however many do not have water quality guidelines in the Australian and New Zealand Guidelines for Fresh and Marine Water Quality. The current study derives ecotoxicity threshold values (ETVs) as unendorsed guideline values for active ingredients in two fungicides (4-hydroxychlorothalonil (fungicide degradate) and carbendazim) and two insecticides (dimethoate and methoxyfenozide) that are commonly detected in GBR catchment waterways. The proposed ETVs have been derived using species sensitivity distributions, as recommended in the Australian and New Zealand nationally endorsed method for deriving water quality guidelines for aquatic ecosystem protection. Four ETVs were derived for each chemical with values that should theoretically protect 99, 95, 90 and 80 % of species (i.e., PC99, PC95, PC90, PC80, respectively). The PC99 and PC95 values for 4-hydroxychlorothalonil, carbendazim, dimethoate and methoxyfenozide were 0.49 µg/L and 4 µg/L, 0.029 µg/L and 0.45 µg/L, 0.11 µg/L and 5.8 µg/L and 0.19 µg/L and 2 µg/L, respectively. The ETVs will be used in an ecological hazard and risk assessment across GBR waterways in part two of this study. The ETVs can also be used to assess potential risk across Australia and internationally where monitoring data are available.

Individual and combined effects of diuron and light reduction on marine microalgae.

Coastal ecosystems such as those in the Great Barrier Reef (GBR) lagoon, are exposed to stressors in flood plumes including low light (caused by increased turbidity) and agricultural pesticides. Photosystem II (PSII)-inhibiting herbicides are the most frequently detected pesticides in the GBR lagoon, but it is not clear how their toxicity to phototrophic species depends on light availability. This study investigated the individual and combined effects of PSII-inhibiting herbicide, diuron, and reduced light intensity (as a proxy for increased turbidity) on the marine diatom, Phaeodactylum tricornutum. Effective quantum yield (EQY) and cell density were measured to calculate responses relative to the controls over 72-h, in tests with varying stressor intensities. Individually, diuron concentrations (0.1-3 µg l-1) were not high enough to significantly reduce growth (cell density), but led to decreased EQY; while, low light generally led to increased EQY, but only reduced growth at the lowest tested light intensity (5 µmol photons m-2 s-1) after 48-hours. P. tricornutum was less affected by diuron when combined with low light scenarios, with increased EQY (up to 163% of the controls) that was likely due to increased electron transport per photon, despite lesser available photons at this low light intensity. In contrast, growth was completely inhibited relative to the controls when algae were simultaneously exposed to the highest stressor levels (3 µg l-1 diuron and 5 µmol photons m-2 s-1). This study highlights the importance of measuring more than one biological response variable to capture the combined effects of multiple stressors. Management of water quality stressors should consider combined impacts rather than just the impacts of individual stressors alone. Reducing suspended sediment and diuron concentrations in marine waters can decrease harmful effects and bring synergistic benefits to water quality.

Interactions among multiple stressors vary with exposure duration and biological response.

Coastal ecosystems are exposed to multiple anthropogenic stressors. Effective management actions would be better informed from generalized predictions of the individual, combined and interactive effects of multiple stressors; however, few generalities are shared across different meta-analyses. Using an experimental study, we present an approach for analysing regression-based designs with generalized additive models that allowed us to capture nonlinear effects of exposure duration and stressor intensity and access interactions among stressors. We tested the approach on a globally distributed marine diatom, using 72 h photosynthesis and growth assays to quantify the individual and combined effects of three common water quality stressors; photosystem II-inhibiting herbicide exposure, dissolved inorganic nitrogen (DIN) enrichment and reduced light (due to excess suspended sediment). Exposure to DIN and reduced light generally resulted in additivity, while exposure to diuron and reduced light resulted in additive, antagonistic or synergistic interactions, depending on the stressor intensity, exposure period and biological response. We thus find the context of experimental studies to be a primary driver of interactions. The experimental and modelling approaches used here bridge the gap between two-way designs and regression-based studies, which provides a way forward to identify generalities in multiple stressor interactions.

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors.

Monitoring the water quality of rivers is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values or trends. However, the data are confounded by anomalies caused by technical issues, for which the volume and velocity of data preclude manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data collected from rivers flowing into the Great Barrier Reef. After identifying end-user needs and defining anomalies, we ranked anomaly importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, incorporation of multiple water-quality variables as covariates reduced performance due to complex relationships among variables. Classifications of drift and periods of anomalously low or high variability were more often correct when we applied mitigation, which replaces anomalous measurements with forecasts for further forecasting, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies and were similarly less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, however, all feature-based methods produced low false positive rates and have the benefit of not requiring training or optimization. Rule-based methods successfully detected a subset of lower priority anomalies, specifically impossible values and missing observations. We therefore suggest that a combination of methods will provide optimal performance in terms of correct anomaly detection, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and anomaly detection developers for optimal outcomes with respect to both detection performance and end-user application. To this end, our framework has high transferability to other types of high frequency time-series data and anomaly detection applications.

Concentrations of levonorgestrel and ethinylestradiol in wastewater effluents: Is the progestin also cause for concern?

Synthetic hormones have been widely reported in treated sewage effluents, and consequently receiving aquatic environments. Ethinylestradiol (EE2) is a potent synthetic estrogen commonly used in conjunction with levonorgestrel in oral contraceptive pills. Both EE2 and levonorgestrel have been identified in the aquatic environment, but although there is a significant amount of literature on EE2, there is much less information on levonorgestrel. Using Australian prescription data as well as excretion and predicted wastewater removal rates, the concentrations of EE2 and levonorgestrel in Australian wastewater were calculated at 0.1 ng/L to 0.5 ng/L and 0.2 ng/L to 0.6 ng/L, respectively. Both compounds were analyzed in treated wastewater and surface water grab samples from 3 Southeast Queensland, Australia sites. The predicted no-effect concentration (PNEC) for EE2 of 0.1 ng/L was exceeded at most sites, with EE2 concentrations up to 2 ng/L in treated effluent, albeit quickly diluted to 0.1 ng/L to 0.2 ng/L in the receiving environment. A provisional PNEC for levonorgestrel of 0.1 ng/L derived in the present study was slightly lower than predicted effluent concentrations of 0.2 ng/L to 0.6 ng/L, indicating a potential risk of endocrine-related effects in exposed aquatic species. The detection limit for levonorgestrel in the present study was 2.5 ng/L, and all samples were below detection limit. The present study's results suggest that improvements in analytical capabilities for levonorgestrel are warranted to more accurately quantify the risk of this compound in the receiving environment. Environ Toxicol Chem 2016;35:1378-1385. © 2015 SETAC.