Opportunities for a probabilistic risk assessment of chemicals in the European Union

Development of a probabilistic risk model for pharmaceuticals in the environment under population and wastewater treatment scenarios

Preparing for future environmental pressures requires projections of how relevant risks will change over time. Current regulatory models of environmental risk assessment (ERA) of pollutants such as pharmaceuticals could be improved by considering the influence of global change factors (e.g., population growth) and by presenting uncertainty more transparently. In this article, we present the development of a prototype object-oriented Bayesian network (BN) for the prediction of environmental risk for six high-priority pharmaceuticals across 36 scenarios: current and three future population scenarios, combined with infrastructure scenarios, in three Norwegian counties. We compare the risk, characterized by probability distributions of risk quotients (RQs), across scenarios and pharmaceuticals. Our results suggest that RQs would be greatest in rural counties, due to the lower development of current wastewater treatment facilities, but that these areas consequently have the most potential for risk mitigation. This pattern intensifies under higher population growth scenarios. With this prototype, we developed a hierarchical probabilistic model and demonstrated its potential in forecasting the environmental risk of chemical stressors under plausible demographic and management scenarios, contributing to the further development of BNs for ERA. Integr Environ Assess Manag 2024;00:1-21. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Development of a Bayesian network for probabilistic risk assessment of pesticides

Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network modeling is explored as an alternative to traditional risk calculation. Bayesian networks can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a Bayesian network has been developed and parameterized for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterization using azoxystrobin as an example. We also demonstrate the seasonal risk calculation for the three pesticides. Integr Environ Assess Manag 2022;18:1072-1087. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

ECORISK2050: An Innovative Training Network for predicting the effects of global change on the emission, fate, effects, and risks of chemicals in aquatic ecosystems

By 2050, the global population is predicted to reach nine billion, with almost three quarters living in cities. The road to 2050 will be marked by changes in land use, climate, and the management of water and food across the world. These global changes (GCs) will likely affect the emissions, transport, and fate of chemicals, and thus the exposure of the natural environment to chemicals. ECORISK2050 is a Marie Skłodowska-Curie Innovative Training Network that brings together an interdisciplinary consortium of academic, industry and governmental partners to deliver a new generation of scientists, with the skills required to study and manage the effects of GCs on chemical risks to the aquatic environment. The research and training goals are to: (1) assess how inputs and behaviour of chemicals from agriculture and urban environments are affected by different environmental conditions, and how different GC scenarios will drive changes in chemical risks to human and ecosystem health; (2) identify short-to-medium term adaptation and mitigation strategies, to abate unacceptable increases to risks, and (3) develop tools for use by industry and policymakers for the assessment and management of the impacts of GC-related drivers on chemical risks. This project will deliver the next generation of scientists, consultants, and industry and governmental decision-makers who have the knowledge and skillsets required to address the changing pressures associated with chemicals emitted by agricultural and urban activities, on aquatic systems on the path to 2050 and beyond.

Tiered ecological risk assessment of nonylphenol and tetrabromobisphenol A in the surface waters of China based on the augmented species sensitivity distribution models

The ecological risks of nonylphenol (NP) and tetrabromobisphenol A (TBBPA) have received continued attention owing to their large consumption, frequently detection, adverse effects on the reproductive fitness, and lack of risk assessment technical systems. The geometric mean of the median concentrations of NP in the 22 surface waters was 0.278 μg/L, and TBBPA in the seven surface waters was 0.014 μg/L in China. The species sensitivity distribution (SSD) models were augmented by extrapolated reproductive toxicity data of native species to reduce uncertainty. The SSD models and the hazardous concentrations for 5% of species exhibited good robustness and reliability using the bootstrap method and minimum sample size determination. The acute and reproductive predicted no-effect concentrations (PNECs) were derived as 9.88 and 0.187 μg/L for NP, and 56.6 and 0.0878 μg/L for TBBPA, respectively. The risk quotients indicated that 11 of 22 locations for NP, and 3 of 7 locations for TBBPA were at high ecological risk levels based on the reproductive PNECs. Furthermore, the higher tier ecological risk assessment (ERA) based on potential affected fraction and joint probability curves indicated that the ecological risks in the four of above locations needed further concern. The ERA based on both the acute and reproductive toxicity is essential for assessing the ecological risks of NP and TBBPA, otherwise using acute PNECs only may result in an underestimation of ecological risk. The developed tiered ERA method and its framework can provide accurate, detailed, quantitative, locally applicable, and economically technical support for ERA of typical endocrine-disrupting chemicals in China.

Probabilistic risk assessment - the keystone for the future of toxicology

Safety sciences must cope with uncertainty of models and results as well as information gaps. Acknowledging this uncertainty necessitates embracing probabilities and accepting the remaining risk. Every toxicological tool delivers only probable results. Traditionally, this is taken into account by using uncertainty / assessment factors and worst-case / precautionary approaches and thresholds. Probabilistic methods and Bayesian approaches seek to characterize these uncertainties and promise to support better risk assessment and, thereby, improve risk management decisions. Actual assessments of uncertainty can be more realistic than worst-case scenarios and may allow less conservative safety margins. Most importantly, as soon as we agree on uncertainty, this defines room for improvement and allows a transition from traditional to new approach methods as an engineering exercise. The objective nature of these mathematical tools allows to assign each methodology its fair place in evidence integration, whether in the context of risk assessment, systematic reviews, or in the definition of an integrated testing strategy (ITS) / defined approach (DA) / integrated approach to testing and assessment (IATA). This article gives an overview of methods for probabilistic risk assessment and their application for exposure assessment, physiologically-based kinetic modelling, probability of hazard assessment (based on quantitative and read-across based structure-activity relationships, and mechanistic alerts from in vitro studies), individual susceptibility assessment, and evidence integration. Additional aspects are opportunities for uncertainty analysis of adverse outcome pathways and their relation to thresholds of toxicological concern. In conclusion, probabilistic risk assessment will be key for constructing a new toxicology paradigm – probably!

Increased Use of Bayesian Network Models Has Improved Environmental Risk Assessments

Environmental and ecological risk assessments are defined as the process for evaluating the likelihood that the environment may be impacted as a result of exposure to stressors. Although this definition implies the calculation of probabilities, risk assessments traditionally rely on nonprobabilistic methods such as calculation of a risk quotient. Bayesian network (BN) models are a tool for probabilistic and causal modeling, increasingly used in many fields of environmental science. Bayesian networks are defined as directed acyclic graphs where the causal relationships and the associated uncertainty are quantified in conditional probability tables. Bayesian networks inherently incorporate uncertainty and can integrate a variety of information types, including expert elicitation. During the last 2 decades, there has been a steady increase in reports on BN applications in environmental risk assessment and management. At recent annual meetings of the Society of Environmental Toxicology and Chemistry (SETAC) North America and SETAC Europe, a number of applications of BN models were presented along with new theoretical developments. Likewise, recent meetings of the European Geosciences Union (EGU) have dedicated sessions to Bayesian modeling in relation to water quality. This special series contains 10 articles based on presentations in these sessions, reflecting a range of BN applications to systems, ranging from cells and populations to watersheds and national scale. The articles report on recent progress in many topics, including climate and management scenarios, ecological and socioeconomic endpoints, machine learning, diagnostic inference, and model evaluation. They demonstrate that BNs can be adapted to established conceptual frameworks used to support environmental risk assessments, such as adverse outcome pathways and the relative risk model. The contributions from EGU demonstrate recent advancements in areas such as spatial (geographic information system [GIS]-based) and temporal (dynamic) BN modeling. In conclusion, this special series supports the prediction that increased use of Bayesian network models will improve environmental risk assessments. Integr Environ Assess Manag 2021;17:53-61. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

A High-Resolution Spatial Model to Predict Exposure to Pharmaceuticals in European Surface Waters: ePiE

Environmental risk assessment of pharmaceuticals requires the determination of their environmental exposure concentrations. Existing exposure modeling approaches are often computationally demanding, require extensive data collection and processing efforts, have a limited spatial resolution, and have undergone limited evaluation against monitoring data. Here, we present ePiE (exposure to Pharmaceuticals in the Environment), a spatially explicit model calculating concentrations of active pharmaceutical ingredients (APIs) in surface waters across Europe at ∼1 km resolution. ePiE strikes a balance between generating data on exposure at high spatial resolution while having limited computational and data requirements. Comparison of model predictions with measured concentrations of a diverse set of 35 APIs in the river Ouse (UK) and Rhine basins (North West Europe), showed around 95% were within an order of magnitude. Improved predictions were obtained for the river Ouse basin (95% within a factor of 6; 55% within a factor of 2), where reliable consumption data were available and the monitoring study design was coherent with the model outputs. Application of ePiE in a prioritisation exercise for the Ouse basin identified metformin, gabapentin, and acetaminophen as priority when based on predicted exposure concentrations. After incorporation of toxic potency, this changed to desvenlafaxine, loratadine, and hydrocodone.

Probabilistic risk assessment of nitrate groundwater contamination from greenhouses in Albenga plain (Liguria, Italy) using lysimeters

The use of fertilizers in greenhouse-grown crops can pose a threat to groundwater quality and, consequently, to human beings and subterranean ecosystem, where intensive farming produces pollutants leaching. Albenga plain (Liguria, Italy) is an alluvial area of about 45km² historically devoted to farming. Recently the crops have evolved to greenhouses horticulture and floriculture production. In the area high levels of nitrates in groundwater have been detected. Lysimeters with three types of reconstituted soils (loamy sand, sandy clay loam and sandy loam) collected from different areas of Albenga plain were used in this study to evaluate the leaching loss of nitrate (NO₃^-) over a period of 12weeks. Leaf lettuce (Lactuca sativa L.) was selected as a representative green-grown crop. Each of the soil samples was treated with a slow release fertilizer, simulating the real fertilizing strategy of the tillage. In order to estimate the potential risk for aquifers as well as for organisms exposed via pore water, nitrate concentrations in groundwater were evaluated by applying a simplified attenuation model to the experimental data. Results were refined and extended from comparison of single effects and exposure values (Tier I level) up to the evaluation of probabilistic distributions of exposure and related effects (Tier II, III IV levels). HHRA suggested HI >1 and about 20% probability of exceeding RfD for all the greenhouses, regardless of the soil. ERA suggested HQ>100 for all the greenhouses; 93% probability of PNEC exceedance for greenhouses containing sand clay loam. The probability of exceeding LC50 for 5% of the species was about 40% and the probability corresponding to DBQ of DEC/EC50>0.001 was >90% for all the greenhouses. The significantly high risk, related to the detected nitrate leaching loss, can be attributed to excessive and inappropriate fertigation strategies.

Probabilistic risk assessment of emerging materials: case study of titanium dioxide nanoparticles

The development and use of emerging technologies such as nanomaterials can provide both benefits and risks to society. Emerging materials may promise to bring many technological advantages but may not be well characterized in terms of their production volumes, magnitude of emissions, behaviour in the environment and effects on living organisms. This uncertainty can present challenges to scientists developing these materials and persons responsible for defining and measuring their adverse impacts. Human health risk assessment is a method of identifying the intrinsic hazard of and quantifying the dose-response relationship and exposure to a chemical, to finally determine the estimation of risk. Commonly applied deterministic approaches may not sufficiently estimate and communicate the likelihood of risks from emerging technologies whose uncertainty is large. Probabilistic approaches allow for parameters in the risk assessment process to be defined by distributions instead of single deterministic values whose uncertainty could undermine the value of the assessment. A probabilistic approach was applied to the dose-response and exposure assessment of a case study involving the production of nanoparticles of titanium dioxide in seven different exposure scenarios. Only one exposure scenario showed a statistically significant level of risk. In the latter case, this involved dumping high volumes of nano-TiO₂ powders into an open vessel with no personal protection equipment. The probabilistic approach not only provided the likelihood of but also the major contributing factors to the estimated risk (e.g. emission potential).

Integrated presentation of ecological risk from multiple stressors

Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic.

The influence of uncertainty and location-specific conditions on the environmental prioritisation of human pharmaceuticals in Europe

The selection of priority APIs (Active Pharmaceutical Ingredients) can benefit from a spatially explicit approach, since an API might exceed the threshold of environmental concern in one location, while staying below that same threshold in another. However, such a spatially explicit approach is relatively data intensive and subject to parameter uncertainty due to limited data. This raises the question to what extent a spatially explicit approach for the environmental prioritisation of APIs remains worthwhile when accounting for uncertainty in parameter settings. We show here that the inclusion of spatially explicit information enables a more efficient environmental prioritisation of APIs in Europe, compared with a non-spatial EU-wide approach, also under uncertain conditions. In a case study with nine antibiotics, uncertainty distributions of the PAF (Potentially Affected Fraction) of aquatic species were calculated in 100∗100km(2) environmental grid cells throughout Europe, and used for the selection of priority APIs. Two APIs have median PAF values that exceed a threshold PAF of 1% in at least one environmental grid cell in Europe, i.e., oxytetracycline and erythromycin. At a tenfold lower threshold PAF (i.e., 0.1%), two additional APIs would be selected, i.e., cefuroxime and ciprofloxacin. However, in 94% of the environmental grid cells in Europe, no APIs exceed either of the thresholds. This illustrates the advantage of following a location-specific approach in the prioritisation of APIs. This added value remains when accounting for uncertainty in parameter settings, i.e., if the 95th percentile of the PAF instead of its median value is compared with the threshold. In 96% of the environmental grid cells, the location-specific approach still enables a reduction of the selection of priority APIs of at least 50%, compared with a EU-wide prioritisation.

Predicting environmental risk: A road map for the future

Frameworks for environmental risk assessment (ERA) focus on comparing results from separate exposure and effect assessments. Exposure assessment generally relies on mechanistic fate models, whereas the effects assessment is anchored in standard test protocols and descriptive statistics. This discrepancy prevents a useful link between these two pillars of ERA, and jeopardizes the realism and efficacy of the entire process. Similar to exposure assessment, effects assessment requires a mechanistic approach to translate the output of fate models into predictions for impacts on populations and food webs. The aim of this study was to discuss (1) the central importance of the individual level, (2) different strategies of dealing with biological complexity, and (3) the role that toxicokinetic-toxicodynamic (TKTD) models, energy budgets, and molecular biology play in a mechanistic revision of the ERA framework. Consequently, an outline for a risk assessment paradigm was developed that incorporates a mechanistic effects assessment in a consistent manner, and a "roadmap for the future." Such a roadmap may play a critical role to eventually arrive at a more scientific and efficient ERA process, and needs to be used to shape our long-term research agendas.

Review of Environmental Assessment Case Studies Blending Elements of Risk Assessment and Life Cycle Assessment

Risk assessment (RA) and life cycle assessment (LCA) are two analytical tools used to support decision making in environmental management. This study reviewed 30 environmental assessment case studies that claimed an integration, combination, hybridization, or complementary use of RA and LCA. The focus of the analysis was on how the respective case studies evaluated emissions of chemical pollutants and pathogens. The analysis revealed three clusters of similar case studies. Yet, there seemed to be little consensus as to what should be referred to as RA and LCA, and when to speak of combination, integration, hybridization, or complementary use of RA and LCA. This paper provides clear recommendations toward a more stringent and consistent use of terminology. Blending elements of RA and LCA offers multifaceted opportunities to adapt a given environmental assessment case study to a specific decision making context, but also requires awareness of several implications and potential pitfalls, of which six are discussed in this paper. To facilitate a better understanding and more transparent communication of the nature of a given case study, this paper proposes a "design space" (i.e., identification framework) for environmental assessment case studies blending elements of RA and LCA. Thinking in terms of a common design space, we postulate, can increase clarity and transparency when communicating the design and results of a given assessment together with its potential strengths and weaknesses.

Integrated probabilistic risk assessment for nanoparticles: the case of nanosilica in food

Insight into risks of nanotechnology and the use of nanoparticles is an essential condition for the social acceptance and safe use of nanotechnology. One of the problems with which the risk assessment of nanoparticles is faced is the lack of data, resulting in uncertainty in the risk assessment. We attempt to quantify some of this uncertainty by expanding a previous deterministic study on nanosilica (5-200 nm) in food into a fully integrated probabilistic risk assessment. We use the integrated probabilistic risk assessment method in which statistical distributions and bootstrap methods are used to quantify uncertainty and variability in the risk assessment. Due to the large amount of uncertainty present, this probabilistic method, which separates variability from uncertainty, contributed to a better understandable risk assessment. We found that quantifying the uncertainties did not increase the perceived risk relative to the outcome of the deterministic study. We pinpointed particular aspects of the hazard characterization that contributed most to the total uncertainty in the risk assessment, suggesting that further research would benefit most from obtaining more reliable data on those aspects.

Deriving persistence indicators from regulatory water-sediment studies – opportunities and limitations in OECD 308 data

The OECD guideline 308 describes a laboratory test method to assess aerobic and anaerobic transformation of organic chemicals in aquatic sediment systems and is an integral part of tiered testing strategies in different legislative frameworks for the environmental risk assessment of chemicals. The results from experiments carried out according to OECD 308 are generally used to derive persistence indicators for hazard assessment or half-lives for exposure assessment. We used Bayesian parameter estimation and system representations of various complexities to systematically assess opportunities and limitations for estimating these indicators from existing data generated according to OECD 308 for 23 pesticides and pharmaceuticals. We found that there is a disparity between the uncertainty and the conceptual robustness of persistence indicators. Disappearance half-lives are directly extractable with limited uncertainty, but they lump degradation and phase transfer information and are not robust against changes in system geometry. Transformation half-lives are less system-specific but require inverse modeling to extract, resulting in considerable uncertainty. Available data were thus insufficient to derive indicators that had both acceptable robustness and uncertainty, which further supports previously voiced concerns about the usability and efficiency of these costly experiments. Despite the limitations of existing data, we suggest the time until 50% of the parent compound has been transformed in the entire system (DegT(50,system)) could still be a useful indicator of persistence in the upper, partially aerobic sediment layer in the context of PBT assessment. This should, however, be accompanied by a mandatory reporting or full standardization of the geometry of the experimental system. We recommend transformation half-lives determined by inverse modeling to be used as input parameters into fate models for exposure assessment, if due consideration is given to their uncertainty.

Developmental neurotoxicity - challenges in the 21st century and in vitro opportunities

In recent years neurodevelopmental problems in children have increased at a rate that suggests lifestyle factors and chemical exposures as likely contributors. When environmental chemicals contribute to neurodevelopmental disorders developmental neurotoxicity (DNT) becomes an enormous concern. But how can it be tackled? Current animal test- based guidelines are prohibitively expensive, at $ 1.4 million per substance, while their predictivity for human health effects may be limited, and mechanistic data that would help species extrapolation are not available. A broader screening for substances of concern requires a reliable testing strategy, applicable to larger numbers of substances, and sufficiently predictive to warrant further testing. This review discusses the evidence for possible contributions of environmental chemicals to DNT, limitations of the current test paradigm, emerging concepts and technologies pertinent to in vitro DNT testing and assay evaluation, as well as the prospect of a paradigm shift based on 21st century technologies.

Predicted no effect concentration derivation as a significant source of variability in environmental hazard assessments of chemicals in aquatic systems: an international analysis

Environmental hazard assessments for chemicals are carried out to define an environmentally "safe" level at which, theoretically, the chemical will not negatively affect any exposed biota. Despite this common goal, the methodologies in use are very diverse across different countries and jurisdictions. This becomes particularly obvious when international scientists work together on documents with global scope, e.g., in the World Health Organization (WHO) International Program on Chemical Safety. In this article, we present a study that describes the extent of such variability and analyze the reasons that lead to different outcomes in deriving a "safe level" (termed the predicted no effect concentration [PNEC] throughout this article). For this purpose, we chose 5 chemicals to represent well-known substances for which sufficient high-quality aquatic effects data were available: ethylene glycol, trichloroethylene, nonylphenol, hexachlorobenzene, and copper (Cu). From these data, 2 data sets for each chemical were compiled: the full data set, that contained all information from selected peer-review sources, and the base data set, a subsample of the full set simulating limited data. Scientists from the European Union (EU), United States, Canada, Japan, and Australia independently carried out hazard assessments for each of these chemicals using the same data sets. Their reasoning for key study selection, use of assessment factors, or use of probabilistic methods was comprehensively documented. The observed variation in the PNECs for all chemicals was up to 3 orders of magnitude, and this was not simply due to obvious factors such as the size of the data set or the methodology used. Rather, this was due to individual decisions of the assessors within the scope of the methodology used, especially key study selection, acute versus chronic definitions, and size of assessment factors. Awareness of these factors, together with transparency of the decision-making process, would be necessary to minimize confusion and uncertainty related to different hazard assessment outcomes, particularly in international documents. The development of a "guideline on transparency in decision-making" ensuring the decision-making process is science-based, understandable, and transparent, may therefore be a promising way forward.

Evolution of the sewage treatment plant model SimpleTreat: use of realistic biodegradability tests in probabilistic model simulations

Given the large number of chemicals under regulatory scrutiny, models play a crucial role in the screening phase of the environmental risk assessment. The sewage treatment plant (STP) model SimpleTreat 3.1 is routinely applied as part of the European Union System for the Evaluation of Substances to estimate the fate and elimination of organic chemicals discharged via sewage. SimpleTreat estimates tend to be conservative and therefore only useful for lower-tier assessments. A probabilistic version of SimpleTreat was built on the updated version of the model (SimpleTreat 3.2, presented in a parallel article in this issue), embracing likeliest as well as worst-case conditions in a statistically robust way. Probabilistic parameters representing the variability of sewage characteristics, STP design, and operational parameters were based on actual STP conditions for activated sludge plants in Europe. An evaluation study was carried out for 4 chemicals with distinct sorption and biodegradability profiles: tonalide, triclosan, trimethoprim, and linear alkylbenzene sulfonate. Simulations incorporated information on biodegradability simulation studies with activated sludge (OECD 314B and OECD 303A tests). Good agreement for both median values and variability ranges was observed between model estimates and monitoring data. The uncertainty analysis highlighted the importance of refined data on partitioning and biodegradability in activated sludge to achieve realistic estimates. The study indicates that the best strategy to refine the exposure assessment of down-the-drain chemicals is by integrating higher-tier laboratory data with probabilistic STP simulations and, if possible, by comparing them with monitoring data for validation.

Uncertainty in QSAR predictions

It is relevant to consider uncertainty in individual predictions when quantitative structure-activity (or property) relationships (QSARs) are used to support decisions of high societal concern. Successful communication of uncertainty in the integration of QSARs in chemical safety assessment under the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system can be facilitated by a common understanding of how to define, characterise, assess and evaluate uncertainty in QSAR predictions. A QSAR prediction is, compared to experimental estimates, subject to added uncertainty that comes from the use of a model instead of empirically-based estimates. A framework is provided to aid the distinction between different types of uncertainty in a QSAR prediction: quantitative, i.e. for regressions related to the error in a prediction and characterised by a predictive distribution; and qualitative, by expressing our confidence in the model for predicting a particular compound based on a quantitative measure of predictive reliability. It is possible to assess a quantitative (i.e. probabilistic) predictive distribution, given the supervised learning algorithm, the underlying QSAR data, a probability model for uncertainty and a statistical principle for inference. The integration of QSARs into risk assessment may be facilitated by the inclusion of the assessment of predictive error and predictive reliability into the "unambiguous algorithm", as outlined in the second OECD principle.

Chlorobenzenes and organochlorinated pesticides in vegetable soils from an industrial site, China

Organochlorinated compounds are ubiquitous contaminants in the environment, especially in industrial sites. The objective of the work was to investigate whether a vegetable field near an industrial site is safe for vegetable production. The residues of chlorobenzenes (CBs), hexachlorocyclohexanes (HCHs) and dichlorodiphenyltrichloroethanes (DDTs) in a vegetable field which was near a chemical plant in China were characterized. Point estimate quotient was used for ecological risk assessment of the investigated site. The results showed that all CBs except monochlorobenzene (MCB) were detected in soils. The total concentrations of sigmaCBs ranged from 71.06 to 716.57 ng/g, with a mean concentration of 434.93 ng/g. The main components of CBs in soil samples were dichlorobenzenes (DCBs), trichlorobenzenes (TCBs) and tetrachlorobenzenes (TeCBs), while for single congeners, 1,2,4-TCB had the highest concentration, which ranged from 13.21 to 210.35 ng/g with a mean concentration of 111.89 ng/g. Residues of hexachlorobenzene (HCB) in soil samples ranged from 0.9 to 11.79 ng/g, significantly lower than sigmaDCB, sigmaTCB and sigmaTeCB. Concentrations of sigmaHCHs and sigmaDDTs in soils ranged from 11.32 to 55.24 ng/g and from 195.63 to 465.58 ng/g, respectively, of which the main components were alpha-HCH and p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE). Ecological risk assessment for the investigated site showed that the most potential risks were from TCBs and TeCBs, based on the hazard quotients. The higher residues of CBs and DDTs compared to the target values and the higher than 1 hazard quotients indicated that this area is not safe for vegetable production and thus soil remediation is needed.

Climate change - An uncertainty factor in risk analysis of contaminated land

Metals frequently occur at contaminated sites, where their potential toxicity and persistence require risk assessments that consider possible long-term changes. Changes in climate are likely to affect the speciation, mobility, and risks associated with metals. This paper provides an example of how the climate effect can be inserted in a commonly used exposure model, and how the exposure then changes compared to present conditions. The comparison was made for cadmium (Cd) exposure to 4-year-old children at a highly contaminated iron and steel works site in southeastern Sweden. Both deterministic and probabilistic approaches (through probability bounds analysis, PBA) were used in the exposure assessment. Potential climate-sensitive variables were determined by a literature review. Although only six of the total 39 model variables were assumed to be sensitive to a change in climate (groundwater infiltration, hydraulic conductivity, soil moisture, soil:water distribution, and two bioconcentration factors), the total exposure was clearly affected. For example, by altering the climate-sensitive variables in the order of 15% to 20%, the deterministic estimate of exposure increased by 27%. Similarly, the PBA estimate of the reasonable maximum exposure (RME, defined as the upper bound of the 95th percentile) increased by almost 20%. This means that sites where the exposure in present conditions is determined to be slightly below guideline values may in the future exceed these guidelines, and risk management decisions could thus be affected. The PBA, however, showed that there is also a possibility of lower exposure levels, which means that the changes assumed for the climate-sensitive variables increase the total uncertainty in the probabilistic calculations. This highlights the importance of considering climate as a factor in the characterization of input data to exposure assessments at contaminated sites. The variable with the strongest influence on the result was the soil:water distribution coefficient (Kd).

Assessing persistence and long-range transport potential of current-use pesticides

Despite the fact that current-use pesticides (CUP) have different chemical properties to first-generation organochlorine products, the long-term and long-range environmental behavior of these chemicals is still unclear. Data for 45 active ingredients of CUPs were collected, most of which originate from the results of simulation tests submitted for authorization. According to the Stockholm Convention on persistent organic pollutants (POPs), two of the 45 CUPs exceed both screening level criteria for persistence and long-range transport potential (LRTP). Thirteen CUPs meet the persistence criterion and just one for LRTP. This classification is compared to the reference chemicals approach using overall persistence (P(ov)) and characteristic travel distance (CTD) calculated with a multimedia model. Although none of the 45 CUP have a CTD above the LRTP boundary line, three of them exceed the overall persistence criterion derived from legacy POPs for classification. Nineteen CUPs are transported over longer distances in water than in air. For such polar substances a LRTP boundary has yet to be defined. We recommend the multimedia model modeling approach to calculate P(ov) and LRTP as a second tier in persistence and LRTP assessment.

Risk assessment of "down-the-drain" chemicals: search for a suitable model

Legal and regulatory authorities around the world generally require that risk assessments are undertaken for the licensing of new and existing substances that present high risk to the environment or the human health. This applies to 'down-the-drain' chemicals that are usually found in household products, such as detergents, that are mainly discharged into rivers via sewer systems. However, the data available for these chemicals is often limited due to cost constraints: in particular, concentration time series for works effluent are generally unavailable, even load data for specific works is often scarce. Although a wide range of models are available, there is a general lack of knowledge on their suitability to model the fate of down-the-drain chemicals at the catchment scale. Several models are presented in this review. The models selected are: the Mackay models, EUSES, Mike 11, QUAL2E, TOMCAT and GREAT-ER. Various applications of these models were investigated to investigate their strength and weaknesses. It appears that, where the availability of data is limited, multimedia fate models such as the Mackay models and EUSES may best be applied to estimate the global risk within each media. However, for site-specific risk assessment the GREAT-ER in-stream water quality model was considered to be more appropriate for modelling down-the-drain chemicals, because it accounts for both spatial and temporal variability, while its data requirements are lower than for models such as Mike 11 and QUAL2E.

Influence of distributional shape of substance parameters on exposure model output

Uncertainty of environmental concentrations is calculated with the regional multimedia exposure model of EUSES 1.0 by considering probability input distributions for aqueous solubility, vapor pressure, and octanol-water partition coefficient, K(ow). Only reliable experimentally determined data are selected from available literature for eight reference chemicals representing a wide substance property spectrum. Monte Carlo simulations are performed with uniform, triangular, and log-normal input distributions to assess the influence of the choice of input distribution type on the predicted concentration distributions. The impact of input distribution shapes on output variance exceeds the effect on the output mean by one order of magnitude. Both are affected by influence and uncertainty (i.e., variance) of the input variable as well. Distributional shape has no influence when the sensitivity function of the respective parameter is perfectly linear. For nonlinear relationships, overlap of probability mass of input distribution with influential ranges of the parameter space is important. Differences in computed output distribution are greatest when input distributions differ in the most influential parameter range.

Probabilistic ecological risk assessment of 1,2,4-trichlorobenzene at a former industrial contaminated site

Measured concentrations of 1,2,4-trichlorobenzene (1,2,4-TCB) in soil and groundwater detected in an industrial contaminated site were used to test several probabilistic options for refining site-specific ecological risks assessment, ranging from comparison of single effects and exposure values through comparison of probabilistic distributions for exposure and effects to the use of distribution based quotients (DBQs) obtained through Monte Carlo simulations. The results of the deterministic approach, which suggest that risk exceeds a level of concern for soil organisms, were influenced mainly by the presence of hot spots reaching concentrations able to affect acutely a large proportion of species, while the large majority of the area presents 1,2,4-TCB concentrations below those reported as toxic. Ground-(pore)water concentrations were compared with aquatic ecotoxicity data in orderto obtain an estimation of the potential risk for aquifers and streams in the adjacent area as well as for soil-dwelling organisms exposed via pore water. In this case, the risk is distributed over a large proportion of the site, while the local risk of hot spots was low, showing that risk characterization based exclusively on soil concentrations might be insufficient.

Sources of uncertainty in pesticide fate modelling

There is worldwide interest in the application of probabilistic approaches to pesticide fate models to account for uncertainty in exposure assessments. The first steps in conducting a probabilistic analysis of any system are: (i) to identify where the uncertainties come from; and (ii) to pinpoint those uncertainties that are likely to affect most of the predictions made. This article aims at addressing those two points within the context of exposure assessment for pesticides through a review of the different sources of uncertainty in pesticide fate modelling. The extensive listing of sources of uncertainty clearly demonstrates that pesticide fate modelling is laced with uncertainty. More importantly, the review suggests that the probabilistic approaches, which are typically being deployed to account for uncertainty in the pesticide fate modelling, such as Monte Carlo modelling, ignore a number of key sources of uncertainty, which are likely to have a significant effect on the prediction of environmental concentrations for pesticides (e.g. model error, modeller subjectivity). Future research should concentrate on quantifying the impact these uncertainties have on exposure assessments and on developing procedures that enable their integration within probabilistic assessments.

Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling

The evaluation of uncertainty is relatively new in environmental life-cycle assessment (LCA). It provides useful information to assess the reliability of LCA-based decisions and to guide future research toward reducing uncertainty. Most uncertainty studies in LCA quantify only one type of uncertainty, i.e., uncertainty due to input data (parameter uncertainty). However, LCA outcomes can also be uncertain due to normative choices (scenario uncertainty) and the mathematical models involved (model uncertainty). The present paper outlines a new methodology that quantifies parameter, scenario, and model uncertainty simultaneously in environmental life-cycle assessment. The procedure is illustrated in a case study that compares two insulation options for a Dutch one-family dwelling. Parameter uncertainty was quantified by means of Monte Carlo simulation. Scenario and model uncertainty were quantified by resampling different decision scenarios and model formulations, respectively. Although scenario and model uncertainty were not quantified comprehensively, the results indicate that both types of uncertainty influence the case study outcomes. This stresses the importance of quantifying parameter, scenario, and model uncertainty simultaneously. The two insulation options studied were found to have significantly different impact scores for global warming, stratospheric ozone depletion, and eutrophication. The thickest insulation option has the lowest impact on global warming and eutrophication, and the highest impact on stratospheric ozone depletion.

Fish bioaccumulation and biomarkers in environmental risk assessment: a review

In this review, a wide array of bioaccumulation markers and biomarkers, used to demonstrate exposure to and effects of environmental contaminants, has been discussed in relation to their feasibility in environmental risk assessment (ERA). Fish bioaccumulation markers may be applied in order to elucidate the aquatic behavior of environmental contaminants, as bioconcentrators to identify certain substances with low water levels and to assess exposure of aquatic organisms. Since it is virtually impossible to predict the fate of xenobiotic substances with simple partitioning models, the complexity of bioaccumulation should be considered, including toxicokinetics, metabolism, biota-sediment accumulation factors (BSAFs), organ-specific bioaccumulation and bound residues. Since it remains hard to accurately predict bioaccumulation in fish, even with highly sophisticated models, analyses of tissue levels are required. The most promising fish bioaccumulation markers are body burdens of persistent organic pollutants, like PCBs and DDTs. Since PCDD and PCDF levels in fish tissues are very low as compared with the sediment levels, their value as bioaccumulation markers remains questionable. Easily biodegradable compounds, such as PAHs and chlorinated phenols, do not tend to accumulate in fish tissues in quantities that reflect the exposure. Semipermeable membrane devices (SPMDs) have been successfully used to mimic bioaccumulation of hydrophobic organic substances in aquatic organisms. In order to assess exposure to or effects of environmental pollutants on aquatic ecosystems, the following suite of fish biomarkers may be examined: biotransformation enzymes (phase I and II), oxidative stress parameters, biotransformation products, stress proteins, metallothioneins (MTs), MXR proteins, hematological parameters, immunological parameters, reproductive and endocrine parameters, genotoxic parameters, neuromuscular parameters, physiological, histological and morphological parameters. All fish biomarkers are evaluated for their potential use in ERA programs, based upon six criteria that have been proposed in the present paper. This evaluation demonstrates that phase I enzymes (e.g. hepatic EROD and CYP1A), biotransformation products (e.g. biliary PAH metabolites), reproductive parameters (e.g. plasma VTG) and genotoxic parameters (e.g. hepatic DNA adducts) are currently the most valuable fish biomarkers for ERA. The use of biomonitoring methods in the control strategies for chemical pollution has several advantages over chemical monitoring. Many of the biological measurements form the only way of integrating effects on a large number of individual and interactive processes in aquatic organisms. Moreover, biological and biochemical effects may link the bioavailability of the compounds of interest with their concentration at target organs and intrinsic toxicity. The limitations of biomonitoring, such as confounding factors that are not related to pollution, should be carefully considered when interpreting biomarker data. Based upon this overview there is little doubt that measurements of bioaccumulation and biomarker responses in fish from contaminated sites offer great promises for providing information that can contribute to environmental monitoring programs designed for various aspects of ERA.

Evaluating and expressing the propagation of uncertainty in chemical fate and bioaccumulation models

First-order analytical sensitivity and uncertainty analysis for environmental chemical fate models is described and applied to a regional contaminant fate model and a food web bioaccumulation model. By assuming linear relationships between inputs and outputs, independence, and log-normal distributions of input variables, a relationship between uncertainty in input parameters and uncertainty in output parameters can be derived, yielding results that are consistent with a Monte Carlo analysis with similar input assumptions. A graphical technique is devised for interpreting and communicating uncertainty propagation as a function of variance in input parameters and model sensitivity. The suggested approach is less calculationally intensive than Monte Carlo analysis and is appropriate for preliminary assessment of uncertainty when models are applied to generic environments or to large geographic areas or when detailed parameterization of input uncertainties is unwarranted or impossible. This approach is particularly useful as a starting point for identification of sensitive model inputs at the early stages of applying a generic contaminant fate model to a specific environmental scenario, as a tool to support refinements of the model and the uncertainty analysis for site-specific scenarios, or for examining defined end points. The analysis identifies those input parameters that contribute significantly to uncertainty in outputs, enabling attention to be focused on defining median values and more appropriate distributions to describe these variables.