Do initial concentration and activated sludge seasonality affect pharmaceutical biotransformation rate constants?

Pharmaceuticals find their way to the aquatic environment via wastewater treatment plants (WWTPs). Biotransformation plays an important role in mitigating environmental risks; however, a mechanistic understanding of involved processes is limited. The aim of this study was to evaluate potential relationships between first-order biotransformation rate constants (kb) of nine pharmaceuticals and initial concentration of the selected compounds, and sampling season of the used activated sludge inocula. Four-day bottle experiments were performed with activated sludge from WWTP Groesbeek (The Netherlands) of two different seasons, summer and winter, spiked with two environmentally relevant concentrations (3 and 30 nM) of pharmaceuticals. Concentrations of the compounds were measured by LC-MS/MS, microbial community composition was assessed by 16S rRNA gene amplicon sequencing, and kb values were calculated. The biodegradable pharmaceuticals were acetaminophen, metformin, metoprolol, terbutaline, and phenazone (ranked from high to low biotransformation rates). Carbamazepine, diatrizoic acid, diclofenac, and fluoxetine were not converted. Summer and winter inocula did not show significant differences in microbial community composition, but resulted in a slightly different kb for some pharmaceuticals. Likely microbial activity was responsible instead of community composition. In the same inoculum, different kb values were measured, depending on initial concentration. In general, biodegradable compounds had a higher kb when the initial concentration was higher. This demonstrates that Michealis-Menten kinetic theory has shortcomings for some pharmaceuticals at low, environmentally relevant concentrations and that the pharmaceutical concentration should be taken into account when measuring the kb in order to reliably predict the fate of pharmaceuticals in the WWTP. KEY POINTS: • Biotransformation and sorption of pharmaceuticals were assessed in activated sludge. • Higher initial concentrations resulted in higher biotransformation rate constants for biodegradable pharmaceuticals. • Summer and winter inocula produced slightly different biotransformation rate constants although microbial community composition did not significantly change.

Variability of Greenhouse Gas Footprints of Field Tomatoes Grown for Processing: Interyear and Intercountry Assessment.

Our study provides an integrated analysis of the variability of greenhouse gas (GHG) footprints of field-grown tomatoes for processing. The global farm-specific data set of 890 observations across 14 countries over a three-year period (2013-2015) was obtained from farms grown under Unilever's sustainable agricultural code. It represents on average 3% of the annual global production of processing tomatoes: insights can be used to help inform corporate sourcing strategies and certification schemes. The median GHG footprint ranged from 18 in Chile to 61 kg CO2-equiv per tonne of tomatoes in India, lower than results reported in other studies. We found that footprints are more consistent within countries than between them. Using linear mixed effect models, we quantified the relative influence of environmental conditions and farm management factors. Key variables were area of production and the method of fertilizer application. GHG footprints decreased with increasing area of production to a threshold of 17.4 ha. Farms using single fertilizer application methods in general had a larger GHG footprint than those using a combination of methods. We conclude that farm management factors should be prioritized for future data collection, and more stringent guidance on acceptable practices is required if greater comparability of outcomes is needed either within a scheme, such as the Unilever's sustainable agriculture code, or between schemes.

Evaluating the Environmental Sustainability of Alternative Ways to Produce Benzene, Toluene, and Xylene.

The petrochemical industry can reduce its environmental impacts by moving from fossil resources to alternative carbon feedstocks. Biomass and plastic waste-based production pathways have recently been developed for benzene, toluene, and xylene (BTX). This study evaluates the environmental impacts of these novel BTX pathways at a commercial and future (2050) scale, combining traditional life cycle assessment with absolute environmental sustainability assessment using the planetary boundary concept. We show that plastic waste-based BTX has lower environmental impacts than fossil BTX, including a 12% decrease in greenhouse gas (GHG) emissions. Biomass-based BTX shows greater GHG emission reductions (42%), but it causes increased freshwater consumption and eutrophication. Toward 2050, GHG emission reductions become 75 and 107% for plastic waste and biobased production, respectively, compared to current fossil-BTX production. When comparing alternative uses of plastic waste, BTX production has larger climate benefits than waste incineration with energy recovery with a GHG benefit of 1.1 kg CO2-equiv/kg plastic waste. For biomass (glycerol)-based BTX production, other uses of glycerol are favorable over BTX production. While alternative BTX production pathways can decrease environmental impacts, they still transgress multiple planetary boundaries. Further impact reduction efforts are thus required, such as using other types of (waste) biomass, increasing carbon recycling, and abatement of end-of-life emissions.

Comparing Environmental Impacts of Single-Junction Silicon and Silicon/Perovskite Tandem Photovoltaics-A Prospective Life Cycle Assessment.

Tandem photovoltaics applying perovskite on silicon are considered to be a possible route to sustaining continuous efficiency improvements and price reductions. A meaningful market share for such tandems is, however, at least a decade away. Herein, a comprehensive prospective life cycle assessment was conducted, comparing the full life cycle of monofacial and bifacial silicon/perovskite tandem panels with single-junction silicon panels produced up to 2050. The end-of-life included the recovery of silicon and silver. Climate change impacts per kilowatt hour were projected to decrease by two-thirds over time. Tandem panels are expected to reach impacts of 8-10 g CO2-eq/kWh in 2050, while single-junction panels may reach 11-13 g CO2-eq/kWh in 2050. Other midpoint impact categories with substantial contributions to damaging human health and ecosystem quality were toxicity, particulate matter formation, and acidification, with tandems having lower impacts in each category. Reductions in impacts over time are mainly the result of grid mix decarbonization and panel efficiency improvements. Balance-of-system and recycling were found to contribute substantially to these impact categories. To ensure that tandem panels provide environmental benefits, annual degradation rates should not exceed 1% for monofacial or 3% for bifacial tandems, and refurbishment of panels with advanced degradation is crucial.

Environmental Impact Prediction of a New Tire Vulcanization Activator.

Zinc oxide (ZnO) is the most common curing activator used to manufacture tires. To minimize environmental impacts by decreasing the zinc content and rolling resistance of tires, ZnO nanoparticles (NPs) anchored on SiO2 NPs (ZnO@SiO2) are currently under development as new activators at the pilot scale. Here, we applied prospective life cycle assessment to predict the impacts on human health, ecosystem quality, and resource scarcity of synthesizing ZnO@SiO2 for the production of passenger car tires at an industrial scale. We found that the life cycle impacts of the synthesis are expected to decrease by 89 to 96% between the pilot and industrial scale. The largest contributors to the synthesis of ZnO@SiO2 were electricity consumption and waste treatment of the solvent. Using the new activator for tire production led to potential reductions of 9 to 12% in life cycle impacts compared to tires that are currently in use. Those reductions were due to the expected decrease in rolling resistance, leading to lower fuel consumption, which outweighed the additional environmental impacts of the synthesis, as well as the potential decrease in lifetime. Our work highlights an opportunity for manufacturers to mitigate their impacts over the full life cycle of the tire.

Quantifying the trade-off between parameter and model structure uncertainty in life cycle impact assessment.

To enhance the use of quantitative uncertainty assessments in life cycle impact assessment practice, we suggest to quantify the trade-off between parameter uncertainty, i.e. any uncertainty associated with data and methods used to quantify the model parameters, and model structure uncertainty, i.e. the uncertainty about the relations and mechanisms being studied. In this paper we show the trade-off between the two types of uncertainty in a case of maize production with a focus on freshwater ecotoxicity due to pesticide application in The Netherlands. Parameter uncertainty in pesticide emissions, chemical-specific data, effect and damage data, and fractions of metabolite formation of degradation products was statistically quantified via probabilistic simulation, i.e. Monte Carlo simulation. Model structure uncertainties regarding the concentration-response model to be included, the selection of the damage model, and the inclusion of pesticide transformation products were assessed via discrete choice analysis. We conclude that to arrive at a minimum level of overall uncertainty the linear concentration-response model is preferable, while the transformation products may be excluded. Selecting the damage model has a relatively low influence on the overall uncertainty. Our study shows that quantifying the trade-off between different types of uncertainty can help to identify optimal model complexity from an uncertainty point of view.

Assessing the importance of spatial variability versus model choices in Life Cycle Impact Assessment: the case of freshwater eutrophication in Europe.

In Life Cycle Impact Assessment (LCIA) both spatial variability and model choices may be influential. In the case of the effect model, the effect factors differ with respect to their assumption of linear/nonlinear responses to increases in environmental stressor levels, and whether or not they account for the current stressor levels in the environment. Here, we derived spatially explicit characterization factors of phosphorus emissions causing eutrophication based on three different effect models (depicted by marginal, linear, and average effect factors) and two freshwater types (lakes and streams) and we performed an analysis of variance (ANOVA) to investigate how the selection of the effect models and the freshwater types influence the impacts of phosphorus emissions to freshwater on heterotrophic species. We found that 56% of the variability of ecological impacts per unit of phosphorus emission was explained, primarily, by the difference between freshwater types and, to a lesser extent, by the difference between effect models. The remaining variability was attributed to the spatial variation between river basins, mainly due to the variability in fate factors. Our study demonstrates the particular importance of accounting for spatial variability and model choices in LCIA.

Predicting and improving the microbial removal of organic micropollutants during wastewater treatment: A review.

Organic micropollutants (OMPs) consist of widely used chemicals such as pharmaceuticals and pesticides that can persist in surface and groundwaters at low concentrations (ng/L to µg/L) for a long time. The presence of OMPs in water can disrupt aquatic ecosystems and threaten the quality of drinking water sources. Wastewater treatment plants (WWTPs) rely on microorganisms to remove major nutrients from water, but their effectiveness at removing OMPs varies. Low removal efficiency might be the result of low concentrations, inherent stable chemical structures of OMPs, or suboptimal conditions in WWTPs. In this review, we discuss these factors, with special emphasis on the ongoing adaptation of microorganisms to degrade OMPs. Finally, recommendations are drawn to improve the prediction of OMP removal in WWTPs and to optimize the design of new microbial treatment strategies. OMP removal seems to be concentration-, compound-, and process-dependent, which poses a great complexity to develop accurate prediction models and effective microbial processes targeting all OMPs.

The potential of emerging bio-based products to reduce environmental impacts.

The current debate on the sustainability of bio-based products questions the environmental benefits of replacing fossil- by bio-resources. Here, we analyze the environmental trade-offs of 98 emerging bio-based materials compared to their fossil counterparts, reported in 130 studies. Although greenhouse gas life cycle emissions for emerging bio-based products are on average 45% lower (-52 to -37%; 95% confidence interval), we found a large variation between individual bio-based products with none of them reaching net-zero emissions. Grouped in product categories, reductions in greenhouse gas emissions ranged from 19% (-52 to 35%) for bioadhesives to 73% (-84 to -54%) for biorefinery products. In terms of other environmental impacts, we found evidence for an increase in eutrophication (369%; 163 to 737%), indicating that environmental trade-offs should not be overlooked. Our findings imply that the environmental sustainability of bio-based products should be evaluated on an individual product basis and that more radical product developments are required to reach climate-neutral targets.

Do interspecies correlation estimations increase the reliability of toxicity estimates for wildlife?

For warm-blooded species, the hazardous dose of a chemical (HD50) is an upcoming and important characteristic in the assessment of toxic chemicals. Generally, experimental information is available for a limited number of warm-blooded species only, which causes statistical uncertainty. Furthermore, when small datasets contain an unrepresentative sample of species, they can cause systematic uncertainty in chemicals' hazardous doses. The number of species can be enlarged with interspecies correlation estimation (ICE) models, but these are uncertain themselves. The goal of this study is to quantify the possible gain in reliability of the HD50 values for warm-blooded wildlife species after enlargement of the sample size with ICE predictions. For 1137 chemicals, we compared systematic uncertainty and statistical uncertainty between HD50 values based on experimental data (HD50(Ex)) and on datasets combining experimental data and ICE predictions (HD50(Co)). HD50(Ex) values ranged between 1.0×10(-1) and 9.5×10(3)mgkg(wwt)(-1), and HD50(Co) values between 1.1×10(0) and 6.1×10(3)mgkg(wwt)(-1). For over 97 percent of the chemicals, HD50(Ex) values exceeded HD50(Co) values, with a systematic uncertainty (i.e. the ratio of HD50(Ex)/HD50(Co)) of typically 3.5. The limited availability of experimental toxicity data, predominantly for mammals, resulted in a systematic underestimation of the wildlife toxicity of a chemical. Statistical uncertainty factors (i.e. the ratio of the 95th/5th percentile) quantified the statistical uncertainty in the HD50 values. The statistical uncertainty factors ranged between 1.0×10(0) and 2.5×10(22) for the experimental dataset, and between 4.8×10(0) and 1.1×10(2) for the combined dataset. For all sample sizes, median statistical uncertainty factors were the largest for combined datasets. However, combining experimental toxicity data with ICE predictions makes it possible to reduce the upper limit of the range for statistical uncertainty factors. We conclude that, by combining experimental data with ICE model predictions, the validity of the HD50 value can be improved and high statistical uncertainty can be reduced, particularly in cases of limited toxicity data, i.e. data for mammals only or a sample size of n≤4.

The many greenhouse gas footprints of green hydrogen.

Green hydrogen could contribute to climate change mitigation, but its greenhouse gas footprint varies with electricity source and allocation choices. Using life-cycle assessment we conclude that if electricity comes from additional renewable capacity, green hydrogen outperforms fossil-based hydrogen. In the short run, alternative uses of renewable electricity likely achieve greater emission reductions.

Implementing groundwater extraction in life cycle impact assessment: characterization factors based on plant species richness for The Netherlands.

An operational method to evaluate the environmental impacts associated with groundwater use is currently lacking in life cycle assessment (LCA). This paper outlines a method to calculate characterization factors that address the effects of groundwater extraction on the species richness of terrestrial vegetation. Characterization factors (CF) were derived for The Netherlands and consist of a fate and an effect part. The fate factor equals the change in drawdown due to a change in groundwater extraction and expresses the amount of time required for groundwater replenishment. It was obtained with a grid-specific steady-state groundwater flow model. Effect factors were obtained from groundwater level response curves of potential plant species richness, which was constructed based on the soil moisture requirements of 625 plant species. Depending on the initial groundwater level, effect factors range up to 9.2% loss of species per 10 cm of groundwater level decrease. The total Dutch CF for groundwater extraction depended on the value choices taken and ranged from 0.09 to 0.61 m(2)·yr/m(3). For tap water production, we showed that groundwater extraction can be responsible for up to 32% of the total terrestrial ecosystem damage. With the proposed approach, effects of groundwater extraction on terrestrial ecosystems can be systematically included in LCA.

Toward meaningful end points of biodiversity in life cycle assessment.

Halting current rates of biodiversity loss will be a defining challenge of the 21st century. To assess the effectiveness of strategies to achieve this goal, indicators and tools are required that monitor the driving forces of biodiversity loss, the changing state of biodiversity, and evaluate the effectiveness of policy responses. Here, we review the use of indicators and approaches to model biodiversity loss in Life Cycle Assessment (LCA), a methodology used to evaluate the cradle-to-grave environmental impacts of products. We find serious conceptual shortcomings in the way models are constructed, with scale considerations largely absent. Further, there is a disproportionate focus on indicators that reflect changes in compositional aspects of biodiversity, mainly changes in species richness. Functional and structural attributes of biodiversity are largely neglected. Taxonomic and geographic coverage remains problematic, with the majority of models restricted to one or a few taxonomic groups and geographic regions. On a more general level, three of the five drivers of biodiversity loss as identified by the Millennium Ecosystem Assessment are represented in current impact categories (habitat change, climate change and pollution), while two are missing (invasive species and overexploitation). However, methods across all drivers can be greatly improved. We discuss these issues and make recommendations for future research to better reflect biodiversity loss in LCA.

Impact of energy flow optimization on the mitigation of environmental consequences and costs in greenhouse cucumber production.

Cucumber, as a major cultivated plant in Iran, takes up more than 80% of greenhouse production. So, improving the production efficiency can save energy, reduce the environmental consequences and production costs, and also create a lot of job opportunities. The present study aimed to investigate the impact of energy flow optimization on the environmental impacts and costs in greenhouse cucumber production by data envelopment analysis (DEA) and life cycle assessment (LCA) methods. For environmental assessment, human health damage, ecosystem damage, and resource scarcity were considered and analyzed in the form of 8, 12, and 2 impact categories, respectively, using the ReCiPe2016 (H) method on endpoint level. Required data were collected through questionnaire from 30 cucumber growers. The results of the current status of environmental impacts showed the major contribution of fuels (natural gas and diesel) in all three main impact categories. Based on DEA analysis, 19 greenhouses (63%) were identified as inefficient units with a mean efficiency score of 0.70 (TE = 0.7). The highest inefficiency was related to the manure (72%), electricity (71.7%), and fuel (59%), respectively. Electricity and manure showed the highest saving potential, while the highest contribution to total energy saving was related to fuel (87%). Environmental analysis of optimized greenhouses showed that efficient use of inputs can reduce all environmental impacts between 7 and 80%, which highlights the importance of efficient resource management in this study. Also, production costs were decreased by about 67 $ tonne-1 (196 to 129 $) after the optimization of the units.

Ammonia and chromate interaction explains unresolved Hyalella azteca mortality in Flanders' sediment bioassays.

Agricultural, industrial and household chemicals are emitted in large rivers along populated areas, transported by water and deposited in sediments, posing (eco)toxicological risks. Sediments have received less attention than surface waters, likely because of the intrinsic complexity of interactions between sediment constituents complicating correct framing of exposures. Sadly, thorough assessment of the in situ behavior of sediment constituents in bioassays is often not practical. Alternatively, we related physicochemical properties of sediments from field testing to results from bioassays. The case study covers Flemish sediment (incl. Scheldt and Meuse) and mortality of Hyalella azteca, a sensitive bio-indicator. Though variable across Flanders' main water bodies, heavy metals and ammoniacal nitrogen dominate the observed toxicity according to toxic unit (TU) assessments. Depending on the water body we explain between 50 and 90% of the variance in the observed H. azteca mortality, substantially more than previous ecotoxicity studies. We attribute the remaining variance to potential incoherently documented biophysicochemical sediment properties and concentrations of non-target biocides, testing conditions/set-ups and/or species variabilities. We discuss the relative influence of heavy metals/metaloxides, nitrogen (e.g. fertilizer), polycyclic aromatics and organochlorides. We highlight both direct and indirect mortality mechanisms. We note potential synergetic mixture effects between ammoniacal nitrogen and chromium. Such synergy may be phenomenological of 'standard' aerobic bioassays, and prove a complementary method alongside the 'acid-volatile sulfide test' to more effectively link concentration to toxicity. Future study ought to include variation in biophysicochemical properties between sampling locations and batch bioassays. Our approach enables water managers to interpret their monitoring data by converting sediment concentrations to H. azteca mortality and prioritize substances that contribute most.

Simulating behavior of petroleum compounds during refinery effluent treatment using the SimpleTreat model.

Distribution and elimination of petroleum products can be predicted in aerobic wastewater treatment plants (WWTPs) using models such as multimedia fate model SimpleTreat. An advantage of the SimpleTreat model is that it only requires a few basic properties of a chemical in wastewater to calculate partitioning, biodegradation and ultimately emissions to air, surface water and produced sludge. The SimpleTreat model structure reflects a WWTP scheme. However, refinery WWTPs typically incorporate more advanced treatment processes such as dissolved air flotation (DAF), a process that clarifies wastewaters by the removal of suspended matter such as oil or solids. The objective of this work was to develop a WWTP removal model that includes DAF treatment. To understand how including a DAF in the model affects the predicted concentrations of petroleum constituents in effluent, we replaced the primary sedimentation module in SimpleTreat with a module simulating DAF. Subsequently, we compared results from the WWTP-DAF model with results obtained with the original SimpleTreat model for a library of over 1500 representative hydrocarbon constituents. The increased air-water exchange in a WWTP-DAF unit resulted in higher predicted removal of volatile constituents. Predicted removal with DAF was on average 17% larger than removal with primary sedimentation. We compared modelled results with measured removal data from the literature, which supported that this model refinement continues to improve the technical basis of assessment of petroleum products.

Chemical footprint of pesticides used in citrus orchards based on canopy deposition and off-target losses.

The application of chemical pesticides is one of the main practices in citrus orchards. But during this operation, a considerable amount of sprayed volume is emitted to off-target areas using air-blast sprayers. The present study aimed to investigate pesticides' toxicological impacts in citrus orchards through determining the proportion of pesticides in different areas (air, soil, and canopy), which facilitate toxicity assessment of pesticides in citrus orchards. In this study, human toxicity and freshwater ecotoxicity impacts were assessed using USEtox 2.1 modeling approach for five active ingredients used in citrus orchards in the south of Iran. Different spraying scenarios were defined based on two types of nozzles (Hallow-cone and Teejet full-cone) and three levels of pressure (30, 40, and 50 bar) in two orchards with different row spacing. Results showed that only 26-37% of spray solution is deposited on the target with much loss to areas between tree canopies on the row. Scenario 1 (Hallow-cone nozzle with spraying pressure as 30 bar) shows the highest spraying efficiency in the both orchards (37% and 34% for Tangerine and Lemon orchards, respectively). Air emissions were obtained around 17 and 18% for hollow-cone and Teejet full-cone nozzles, respectively. The final inventory was obtained considering evaporation rate of active ingredients from soil surface and leaves. Based on the results obtained from toxicological impact assessment, Thiacloprid and Carbendazim had the highest negative environmental impacts on human health and freshwater aquatic ecosystem, respectively. Based on the results, soil emissions were identified as more critical than air emissions in terms of environmental consequences. It can be attributed to the higher characterization factor and deposition on the soil in comparison to the air. The present study provided well-founded information on the environmental performance of production systems by estimating the relevant emissions of pesticides to different compartments and determined the human and freshwater toxicity impact profiles, which assist decision-makers and LCA-practitioners to have a better perspective about pesticides behavior in receiving compartment.

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.

Global relative species loss due to first-generation biofuel production for the transport sector.

The global demand for biofuels in the transport sector may lead to significant biodiversity impacts via multiple human pressures. Biodiversity assessments of biofuels, however, seldom simultaneously address several impact pathways, which can lead to biased comparisons with fossil fuels. The goal of the present study was to quantify the direct influence of habitat loss, water consumption and greenhouse gas (GHG) emissions on potential global species richness loss due to the current production of first-generation biodiesel from soybean and rapeseed and bioethanol from sugarcane and corn. We found that the global relative species loss due to biofuel production exceeded that of fossil petrol and diesel production in more than 90% of the locations considered. Habitat loss was the dominating stressor with Chinese corn, Brazilian soybean and Brazilian sugarcane having a particularly large biodiversity impact. Spatial variation within countries was high, with 90th percentiles differing by a factor of 9 to 22 between locations. We conclude that displacing fossil fuels with first-generation biofuels will likely negatively affect global biodiversity, no matter which feedstock is used or where it is produced. Environmental policy may therefore focus on the introduction of other renewable options in the transport sector.

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

The use of down-the-drain products and the resultant release of chemicals may lead to pressures on the freshwater environment. Ecotoxicological impact assessment is a commonly used approach to assess chemical products but is still influenced by several uncertainty and variability sources. As a result, the robustness and reliability of such assessments can be questioned. A comprehensive and systematic assessment of these sources is, therefore, needed to increase their utility and credibility. In this study, we present a method to systematically analyse the uncertainty and variability of the potential ecotoxicological impact (PEI) of chemicals using a portfolio of 54 shampoo products. We separately quantified the influence of the statistical uncertainty in the prediction of physicochemical properties and freshwater toxicity as predicted from Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) respectively, and of various sources of spatial and technological variability as well as variability in consumer habits via 2D Monte Carlo simulations. Overall, the variation in the PEIs of shampoo use was mainly the result of uncertainty due to the use of toxicity data from three species only. All uncertainty sources combined resulted in PEIs ranging on average over seven orders of magnitude (ratio of the 90th to the 10th percentile) so that an absolute quantification of the ecological risk would not be meaningful. In comparison, variation in shampoo composition was the most influential source of variability, although less than compared to uncertainty, leading to PEIs ranging over three orders of magnitude. Increasing the number of toxicity data by increasing the number of species, either through additional measurements or ecotoxicological modelling (e.g. using Interspecies Correlation Equations), should get priority to improve the reliability of PEIs. These conclusions are not limited to shampoos but are applicable more generally to the down-the-drain products since they all have similar data limitations and associated uncertainties relating to the availability of ecotoxicity data and variability in consumer habits and use.