Confronting variability with uncertainty in the ecotoxicological impact assessment of down-the-drain products

Organoleptic and physicochemical properties of natural-based herbal shampoo formulations with Cyclea peltata as a key ingredient

BACKGROUND

Synthetic shampoos have toxic chemical agents like chemicals such as parabens and sodium lauryl sulfate which damage the hair. To combat this issue, the use of natural herbal shampoos, as opposed to synthetic shampoos, is becoming increasingly popular. One of the many medicinal plants present in Karnataka and Kerala is Cyclea peltata. This plant has long been known to assist in wound healing, allergy relief, and hair and scalp improvement, as well as serving as a coolant for hair applications.

OBJECTIVE

The key subject of this research was the preparation of plant extracts and fermentation of Cyclea peltata leaf extract with Lactobacillus plantarum, resulting in the formulation of an herbal shampoo.

METHOD

The leaf extract contained alkaloids, flavonoids, tannins, phenolic acid, saponins, and proteins, according to previous research by this group. The fermented product showed the presence of lactic acid, which was a boon for shampoo base as it protects the hair from the sunlight and improves the texture and strength of hair fibers. Cyclea peltata was used as the main ingredient in herbal shampoo formulations, along with other herbal ingredients, and the formulated shampoo was characterized for its different organoleptic (Color, odor) and physicochemical properties (pH, solid content, wettability, emulsification, and foam stability) to find the best formulation.

RESULT

Formulation H2 showed better organoleptic and physicochemical properties and thus was chosen as the best formulation among the 11 formulations.

CONCLUSION

Formulation H2 is regarded as the best formulation based on the results of the physicochemical tests (Pleasant odor, pH 3.01, solid content of 5.75%, wettability of 1.68 s, and percentage emulsification of 68.75%).

Comparing in vivo data and in silico predictions for acute effects assessment of biocidal active substances and metabolites for aquatic organisms

The purpose of this study was to determine the acute toxicity in aquatic organisms of one biocidal active substance and six metabolites derived from biocidal active substances and to assess the suitability of available QSAR models to predict the obtained values. We have reported the acute toxicity in sewage treatment plant (STP) microorganisms, in the freshwater microalgae Pseudokirchneriella subcapitata and in Daphnia magna following OECD test methods. We have also identified in silico models for acute toxicity of these trophic levels currently available in widely recognized platforms such as VEGA and the OECD QSAR ToolBox. A total of six, four and two models have been selected for Daphnia, algae and microorganisms, respectively. Finally, we have compared the in silico and in vivo data for the seven compounds plus two previously assayed biocidal substances. None of the compounds tested were toxic for Daphnia and STP microorganisms. For microalgae, CGA71019 (1,2,4 triazole) presented an ErC₅₀ value of 38.3 mg/L. The selected in silico models have provided lower EC₅₀ values and are therefore more conservative. Models from the OECD QSAR ToolBox predicted values for 7 out of 9 and for 4 out of 9 chemicals for Daphnia and P. subcapitata, respectively. No predictive models were identified in such platform for STP microorganism's acute effects. In terms of models's specificity, biocide-specific models, developed from curated datasets integrated by biocidal active substances and implemented in VEGA, perform better in the case of microalgae but for Daphnia an alternative, non biocide-specific has revealed a better performance. For STP microorganisms only biocide-specific models have been identified.

Aquatic testing guidelines insufficiently control the influence of dilution water toc and hardness on cationic polymer toxicity - A proposal to improve standardized test procedures

Cationic polymers (CPs) are widely used chemicals for wastewater treatment applications and in various "down-the-drain" household products. The aquatic toxicity of CPs results from an electrostatic interaction with negatively charged cell surfaces. These effects are greatly mitigated by the binding affinity of CPs to total organic carbon (TOC) in surface water. Consequently, baseline aquatic toxicity tests of CPs using clean lab water (TOC < 2 mg/L) typically overestimate toxicity and risk which is greatly mitigated at higher environmentally relevant OC levels. However, the point at which mitigation begins is not well defined and low-level TOC in lab water may influence the baseline toxicity outcome. Similarly, divalent cations, quantified as water hardness, may modulate the electrostatic binding between OC and CP. Although standard guidelines define limits for lab water hardness and TOC, the consequences of variability within those limits on test outcome is unknown. We investigated the impact of part-per-billion (ppb) additions of TOC to lab water at different hardness levels on CP acute toxicity to Daphnia magna and Raphidocelis subcapitata. In both species, the acute toxicities of CPs with different molecular weight and charge density varied by > 10-fold in response to slight changes in TOC and water hardness, although parameters were maintained within guideline limits. When determining the baseline aquatic toxicity of CPs, the lab water should be standardized at the lowest biologically tolerable hardness and TOC at a reliably measurable level (>1 - < 2 mg/L) to reduce variability and increase the reliability of the toxicity estimate.

An (Eco)Toxicity Life Cycle Impact Assessment Framework for Per- And Polyfluoroalkyl Substances

A framework for characterizing per- and polyfluoroalkyl substances (PFASs) in life cycle impact assessment (LCIA) is proposed. Thousands of PFASs are used worldwide, with special properties imparted by the fluorinated alkyl chain. Our framework makes it possible to characterize a large part of the family of PFASs by introducing transformation fractions that translate emissions of primary emitted PFASs into the highly persistent terminal degradation products: the perfluoroalkyl acids (PFAAs). Using a PFAA-adapted characterization model, human toxicity as well as marine and freshwater aquatic ecotoxicity characterization factors are calculated for three PFAAs, namely perfluorooctanoic acid (PFOA) perfluorohexanoic acid (PFHxA) and perfluorobutanesulfonic acid (PFBS). The model is evaluated to adequately capture long-term fate, where PFAAs are predicted to accumulate in open oceans. The characterization factors of the three PFAAs are ranked among the top 5% for marine ecotoxicity, when compared to 3104 chemicals in the existing USEtox results databases. Uncertainty analysis indicates potential for equally high ranks for human health impacts. Data availability constitutes an important limitation creating uncertainties. Even so, a life cycle assessment (LCA) case study illustrates practical application of our proposed framework, demonstrating that even low emissions of PFASs can have large effects on LCA results.

Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations

A reliable quantification of the potential effects of chemicals on freshwater ecosystems requires ecotoxicological response data for a large set of species which is typically not available in practice. In this study, we propose a method to estimate hazardous concentrations (HCs) of chemicals on freshwater ecosystems by combining two in silico approaches: quantitative structure activity relationships (QSARs) and interspecies correlation estimation (ICE) models. We illustrate the principle of our QSAR-ICE method by quantifying the HCs of 51 chemicals at which 50% and 5% of all species are exposed above the concentration causing acute effects. We assessed the bias of the HCs, defined as the ratio of the HC based on measured ecotoxicity data and the HC based on in silico data, as well as the statistical uncertainty, defined as the ratio of the 95th and 5th percentile of the HC. Our QSAR-ICE method resulted in a bias that was comparable to the use of measured data for three species, as commonly used in effect assessments: the average bias of the QSAR-ICE HC50 was 1.2 and of the HC5 2.3 compared to 1.2 when measured data for three species were used for both HCs. We also found that extreme statistical uncertainties (>10⁵) are commonly avoided in the HCs derived with the QSAR-ICE method compared to the use of three measurements with statistical uncertainties up to 10¹². We demonstrated the applicability of our QSAR-ICE approach by deriving HC50s for 1,223 out of the 3,077 organic chemicals of the USEtox database. We conclude that our QSAR-ICE method can be used to determine HCs without the need for additional in vivo testing to help prioritise which chemicals with no or few ecotoxicity data require more thorough assessment.

Spatial variability of ecosystem exposure to home and personal care chemicals in Asia

It is well recognized that there are currently limitations in the spatial and temporal resolution of environmental exposure models due to significant variabilities and uncertainties in model inputs and parameters. Here we present the updated Pangea multi-scale multimedia model based on the more spatially resolved, catchment-based hydrological HydroBASINS dataset covering the entire globe. We apply it to predict spatially-explicit exposure concentrations of linear alkylbenzene sulphonate (LAS) and triclosan (TCS) as two chemicals found in homecare (HC) and personal care (PC) products in river catchments across Asia, and test its potential for identifying/prioritizing catchments with higher exposure concentrations. In addition, we also identify the key parameters in the model framework driving higher concentrations and perform uncertainty analyses by applying Monte Carlo simulations on emissions and other non-spatial model inputs. The updated combination of Pangea with the HydroBASINS hydrological data represents a substantial improvement from the previous model with the gridded hydrological dataset (WWDRII) for modelling substance fate, with higher resolution and improved coverage in regions with lower flows, with the results demonstrating good agreement with monitored concentrations for TCS in both the freshwater (R² = 0.55) and sediment (R² = 0.81) compartments. The ranking of water basins by Predicted Environmental Concentrations (PECs) was similar for both TCS and LAS, with highest concentrations (Indus, Huang He, Cauvery, Huai He and Ganges) being one to two orders of magnitude greater than the water basins with lowest predicted PECs (Mekong and Brahmaputra). Emissions per unit volume of each catchment, chemical persistence, and river discharge were deemed to be the most influential factors on the variation of predicted PECs. Focusing on the Huang He (Yellow River) water basin, uncertainty confidence intervals (factor 31 for LAS and 6 for TCS) are much lower than the variability of predicted PECs across the Huang He catchments (factors 90,700 for LAS and 13,500 for TCS).

Environmental co-benefits and adverse side-effects of alternative power sector decarbonization strategies

A rapid and deep decarbonization of power supply worldwide is required to limit global warming to well below 2 °C. Beyond greenhouse gas emissions, the power sector is also responsible for numerous other environmental impacts. Here we combine scenarios from integrated assessment models with a forward-looking life-cycle assessment to explore how alternative technology choices in power sector decarbonization pathways compare in terms of non-climate environmental impacts at the system level. While all decarbonization pathways yield major environmental co-benefits, we find that the scale of co-benefits as well as profiles of adverse side-effects depend strongly on technology choice. Mitigation scenarios focusing on wind and solar power are more effective in reducing human health impacts compared to those with low renewable energy, while inducing a more pronounced shift away from fossil and toward mineral resource depletion. Conversely, non-climate ecosystem damages are highly uncertain but tend to increase, chiefly due to land requirements for bioenergy.

Comparing Options for Deriving Chemical Ecotoxicity Hazard Values for the European Union Environmental Footprint, Part II

Using the European Union's Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) ecotoxicity data, this paper compares 3 different approaches to calculate final substance toxicity hazard values using the USEtox approach (chronic EC50 + acute EC50/2), using only acute EC50 equivalent data (EC50_eq ), and using only chronic no observed effect concentration equivalent (NOEC_eq) data. About 4008, 4853, and 5560 substance hazard values could be calculated for the USEtox model, acute only, and chronic only approaches, respectively. The USEtox model provides hazard values similar to the ones based on acute EC50 data only. Although there is a large amount of variability in the ratios, the data support acute EC50_eq to chronic NOEC_eq ratios (calculated as geometric mean) of 10.64, 10.90, and 4.21 for fish, crustaceans, and algae respectively. Comparison of the calculated hazard values with the criteria used by the EU chemical Classification, Labelling, and Packaging regulation (CLP) shows the USEtox model underestimates the number of compounds categorized as very toxic to aquatic life and/or having long-lasting effects. In contrast, use of the chronic NOEC data shows a good agreement with CLP. It is therefore proposed that chronic NOEC_eq are used to derive substance hazard values to be used in the EU Environmental Footprint. Due to poor data availability for some chemicals, the uncertainty of the final hazard values is expected to be high. Integr Environ Assess Manag 2019;15:796-807. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).