Bioassay predictive values for chemical health risks in drinking water.

Bioanalytical tools can be used for assessment of the chemical quality of drinking water and its sources. For water managers it is important to know the probability that a bioassay response above an established health-based 'effect-based trigger value' (EBT) indeed implies a harmful chemical (mixture) concentration. This study presents and applies a framework, based on Bayes' theorem, to derive such risk probabilities for bioassay responses. These were evaluated under varying (in silico) chemical mixture concentrations relevant to drinking water (sources), with toxicity data for six in vitro assays from the ToxCast database. For single chemicals and in silico mixtures, the negative predictive value (NPV) was 100 % for all assays. For water managers, this means that when a bioassay response is below the EBT, a chemical risk is reliably absent, and no further action is required. The positive predictive value (PPV) increased with increasing chemical concentrations (2 µg/L) up to 40-80 %, depending on the assay. For in silico mixtures of increasing numbers of chemicals, the PPV did not increase until higher sum concentrations (>2-10 µg/L). Hence, the ability to accurately signal a harmful chemical (mixture) using bioassays will be lowest for highly diverse, low-concentration chemical mixtures. For water managers, this means in practice that further investigations after an EBT exceedance will, in many cases, not reveal chemicals at harmful concentrations. A solution offered is to increase the trigger value for positive responses to achieve a higher PPV and maintain the EBT for negative responses to ensure an optimal NPV.

Simulating changes in polar bear subpopulation growth rate due to legacy persistent organic pollutants - Temporal and spatial trends.

Although atmospheric concentrations of many conventional persistent organic pollutants (POPs) have decreased in the Arctic over the past few decades, levels of most POPs and mercury remain high since the 1990s or start to increase again in Arctic areas, especially polar bears. So far, studies generally focused on individual effects of POPs, and do not directly link POP concentrations in prey species to population-specific parameters. In this study we therefore aimed to estimate the effect of legacy POPs and mercury on population growth rate of nineteen polar bear subpopulations. We modelled population development in three scenarios, based on species sensitivity distributions (SSDs) derived for POPs based on ecotoxicity data for endothermic species. In the first scenario, ecotoxicity data for polar bears were based on the HC50 (the concentration at which 50% of the species is affected). The other two scenarios were based on the HC5 and HC95. Considerable variation in effects of POPs could be observed among the scenarios. In our intermediate scenario, we predicted subpopulation decline for ten out of 15 polar bear subpopulations. The estimated population growth rate was least reduced in Gulf of Boothia and Foxe Basin. On average, PCB concentrations in prey (in µg/g toxic equivalency (TEQ)) posed the largest threat to polar bear subpopulations, with negative modelled population growth rates for the majority of subpopulations. We did not find a correlation between modelled population changes and monitored population trends for the majority of chemical-subpopulation combinations. Modelled population growth rates increased over time, implying a decreasing effect of PCBs, DDTs, and mercury. Polar bear subpopulations are reportedly still declining in four out of the seven subpopulations for which sufficient long-term monitoring data is available, as reported by the IUCN-PBSG. This implies that other emerging pollutants or other anthropogenic stressors may affect polar bear subpopulations.

Implications of Trophic Variability for Modeling Biomagnification of POPs in Marine Food Webs in the Svalbard Archipelago.

The occurrence of persistent organic pollutants (POPs) in the Arctic has been of constant concern, as these chemicals cause reproductive effects and mortality in organisms. The Arctic acts as a chemical sink, which makes this system an interesting case for bioaccumulation studies. However, as conducting empirical studies for all Arctic species and POPs individually is unfeasible, in silico methods have been developed. Existing bioaccumulation models are predominately validated for temperate food chains, and do not account for a large variation in trophic levels. This study applies Monte Carlo simulations to account for variability in trophic ecology on Svalbard when predicting bioaccumulation of POPs using the optimal modeling for ecotoxicological applications (OMEGA) bioaccumulation model. Trophic magnification factors (TMFs) were calculated accordingly. Comparing our model results with monitored POP residues in biota revealed that, on average, all predictions fell within a factor 6 of the monitored POP residues in biota. Trophic variability did not affect model performance tremendously, with up to a 25% variability in performance metrics. To our knowledge, we were the first to include trophic variability in predicting biomagnification in Arctic ecosystems using a mechanistic biomagnification model. However, considerable amounts of data are required to quantify the implications of trophic variability on biomagnification of POPs in Arctic food webs.

Chemical fate of persistent organic pollutants in the arctic: Evaluation of simplebox.

Persistent organic pollutants (POPs) are of great concern for decades due to their persistence, bioaccumulation and long-range transport potential. Multimedia fate models are useful scientific and decision-support tools for predicting the chemical fate in the environment. The SimpleBox multimedia fate model (v4.0) was used in this study to estimate the impact of POP emissions from the European and North American mainland on POP contamination in the Arctic. The purpose of the study was to evaluate the performance of SimpleBox by comparing estimations to measurements. Model performance for the air compartment was reasonable as estimated concentrations were generally within a factor of five of measured concentrations. SimpleBox suggested higher POP concentrations in Arctic oceans than in temperate oceans, contrary to the few measured data. Discrepancies between estimations and measurements may be attributed to the variability in emission estimates and degradation rates of POPs, representativeness of monitoring data, and a missing snow and ice environmental compartment in SimpleBox. Emission rates and degradation rate constants were the most influential input parameters in SimpleBox based on sensitivity analysis. Suggestions for improvements of SimpleBox refining POP risk assessment are provided.

QSAR-Based Estimation of Species Sensitivity Distribution Parameters: An Exploratory Investigation.

Ecological risk assessments are hampered by limited availability of ecotoxicity data. The present study aimed to explore the possibility of deriving species sensitivity distribution (SSD) parameters for nontested compounds, based on simple physicochemical characteristics, known SSDs for data-rich compounds, and a quantitative structure-activity relationship (QSAR)-type approach. The median toxicity of a data-poor chemical for species assemblages significantly varies with values of the physicochemical descriptors, especially when based on high-quality SSD data (from either acute median effect concentrations or chronic no-observed-effect concentrations). Beyond exploratory uses, we discuss how the precision of QSAR-based SSDs can be improved to construct models that accurately predict the SSD parameters of data-poor chemicals. The current models show that the concept of QSAR-based SSDs supports screening-level evaluations of the potential ecotoxicity of compounds for which data are lacking. Environ Toxicol Chem 2019;38:2764-2770. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Deriving Field-Based Ecological Risks for Bird Species.

Ecological risks (ERs) of pollutants are typically assessed using species sensitivity distributions (SSDs), based on effect concentrations obtained from bioassays with unknown representativeness for field conditions. Alternatively, monitoring data relating breeding success in bird populations to egg concentrations may be used. In this study, we developed a procedure to derive SSDs for birds based on field data of egg concentrations and reproductive success. As an example, we derived field-based SSDs for p, p'-DDE and polychlorinated biphenyls (PCBs) exposure to birds. These SSDs were used to calculate ERs for these two chemicals in the American Great Lakes and the Arctic. First, we obtained field data of p, p'-DDE and PCBs egg concentrations and reproductive success from the literature. Second, these field data were used to fit exposure-response curves along the upper boundary (right margin) of the response's distribution (95th quantile), also called quantile regression analysis. The upper boundary is used to account for heterogeneity in reproductive success induced by other external factors. Third, the species-specific EC10/50s obtained from the field-based exposure-response curves were used to derive SSDs per chemical. Finally, the SSDs were combined with specific exposure data for both compounds in the two areas to calculate the ER. We found that the ERs of combined exposure to these two chemicals were a factor of 5-35 higher in the Great Lakes compared to Arctic regions. Uncertainty in the species-specific exposure-response curves and related SSDs was mainly caused by the limited number of field exposure-response data for bird species. With sufficient monitoring data, our method can be used to quantify field-based ecological risks for other chemicals, species groups, and regions of interest.

Using field data to quantify chemical impacts on wildlife population viability.

Environmental pollution is an important driver of biodiversity loss. Yet, to date, the effects of chemical exposure on wildlife populations have been quantified for only a few species, mainly due to a lack of appropriate laboratory data to quantify chemical impacts on vital rates. In this study, we developed a method to quantify the effects of toxicant exposure on wildlife population persistence based on field monitoring data. We established field-based vital-rate-response functions for toxicants, using quantile regression to correct for the influences of confounding factors on the vital rates observed, and combined the response curves with population viability modelling. We then applied the method to quantify the impact of DDE on three bird species: the White-tailed Eagle, Bald Eagle, and Osprey. Population viability was expressed via five population extinction vulnerability metrics: population growth rate (r1 ), critical patch size (CPS), minimum viable population size (MVP), probability of population extirpation (PE), and median time to population extirpation (MTE). We found that past DDE exposure concentrations increased population extirpation vulnerabilities of all three bird species. For example, at DDE concentrations of 25 mg/kg wet mass of egg (the maximum historic exposure concentration reported in literature for the Osprey), r1 became small (White-tailed Eagle and Osprey) or close to zero (Bald Eagle), the CPS increased up to almost the size of Connecticut (White-tailed Eagle and Osprey) or West Virginia (Bald Eagle), the MVP increased up to approximately 90 (White-tailed Eagle and Osprey) or 180 breeding pairs (Bald Eagle), the PE increased up to almost certain extirpation (Bald Eagle) or only slightly elevated levels (White-tailed Eagle and Osprey) and the MTE became within decades (Bald Eagle) or remained longer than a millennium (White-tailed Eagle and Osprey). Our study provides a method to derive species-specific field-based response curves of toxicant exposure, which can be used to assess population extinction vulnerabilities and obtain critical levels of toxicant exposure based on maximum permissible effect levels. This may help conservation managers to better design appropriate habitat restoration and population recovery measures, such as reducing toxicant levels, increasing the area of suitable habitat or reintroducing individuals.