Land degradation neutrality: Testing the indicator in a temperate agricultural landscape.

Land degradation directly affects around 25% of land globally, undermining progress on most of the UN Sustainable Development Goals (SDG), particularly target 15.3. To assess land degradation, SDG indicator 15.3.1 combines sub-indicators of productivity, soil carbon and land cover. Over 100 countries have set Land Degradation Neutrality (LDN) targets. Here, we demonstrate application of the indicator for a well-established agricultural landscape using the case study of Great Britain. We explore detection of degradation in such landscapes by: 1) transparently evaluating land cover transitions; 2) comparing assessments using global and national data; 3) identifying misleading trends; and 4) including extra sub-indicators for additional forms of degradation. Our results demonstrate significant impacts on the indicator both from the land cover transition evaluation and choice or availability of data. Critically, we identify a misleading improvement trend due to a trade-off between improvement detected by the productivity sub-indicator, and 30-year soil carbon loss trends in croplands (11% from 1978 to 2007). This carbon loss trend would not be identified without additional data from Countryside Survey (CS). Thus, without incorporating field survey data we risk overlooking the degradation of regulating and supporting ecosystem services (linked to soil carbon), in favour of signals from improving provisioning services (productivity sub-indicator). Relative importance of these services will vary between socioeconomic contexts. Including extra sub-indicators for erosion or critical load exceedance, as additional forms of degradation, produced a switch from net area improving (9%) to net area degraded (58%). CS data also identified additional degradation for soil health, including 44% arable soils exceeding bulk density thresholds and 35% of CS squares exceeding contamination thresholds for metals.

Key principles and operational practices for improved nanotechnology environmental exposure assessment.

Nanotechnology is identified as a key enabling technology due to its potential to contribute to economic growth and societal well-being across industrial sectors. Sustainable nanotechnology requires a scientifically based and proportionate risk governance structure to support innovation, including a robust framework for environmental risk assessment (ERA) that ideally builds on methods established for conventional chemicals to ensure alignment and avoid duplication. Exposure assessment developed as a tiered approach is equally beneficial to nano-specific ERA as for other classes of chemicals. Here we present the developing knowledge, practical considerations and key principles need to support exposure assessment for engineered nanomaterials for regulatory and research applications.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment.

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

Comparison of four methods for bioavailability-based risk assessment of mixtures of Cu, Zn, and Ni in freshwater.

Although chemical risk assessment is still mainly conducted on a substance-by-substance basis, organisms in the environment are typically exposed to mixtures of substances. Risk assessment procedures should therefore be adapted to fit these situations. Four mixture risk assessment methodologies were compared for risk estimations of mixtures of copper (Cu), zinc (Zn), and nickel (Ni). The results showed that use of the log-normal species sensitivity distribution (SSD) instead of the best-fit distribution and sampling species sensitivities independently for each metal instead of using interspecies correlations in metal sensitivity had little impact on risk estimates. Across 4 different monitoring datasets, between 0% and 52% of the target water samples were estimated to be at risk, but only between 0% and 15% of the target water samples were at risk because of the mixture of metals and not any single metal individually. When a natural baseline database was examined, it was estimated that 10% of the target water samples were at risk because of single metals or their mixtures; when the most conservative method was used (concentration addition [CA] applied directly to the SSD, i.e., CASSD ). However, the issue of metal mixture risk at geochemical baseline concentrations became relatively small (2% of target water samples) when a theoretically more correct method was used (CA applied to individual dose response curves, i.e., CADRC ). Finally, across the 4 monitoring datasets, the following order of conservatism for the 4 methods was shown (from most to least conservative, with ranges of median margin of safety [MoS] relative to CASSD ): CASSD > CADRC (MoS = 1.17-1.25) > IADRC (independent action (IA) applied to individual dose-response curves; MoS = 1.38-1.60) > IASSD (MoS = 1.48-1.72). Therefore, it is suggested that these 4 methods can be used in a general tiered scheme for the risk assessment of metal mixtures in a regulatory context. In this scheme, the CASSD method could serve as a first (conservative) tier to identify situations with likely no potential risk at all, regardless of the method used (the sum toxic unit expressed relative to the 5% hazardous concentration [SumTUHC5 ] < 1) and the IASSD method to identify situations of potential risk, also regardless of the method used (the multisubstance potentially affected fraction of species using the IASSD method [msPAFIA,SSD ] > 0.05). The CADRC and IADRC methods could be used for site-specific assessment for situations that fall in between (SumTUHC5 > 1 and msPAFIA,SSD < 0.05). Environ Toxicol Chem 2017;36:2123-2138. © 2017 SETAC.

Assessment of co-contaminant effects on uranium and thorium speciation in freshwater using geochemical modelling.

Speciation modelling of uranium (as uranyl) and thorium, in four freshwaters impacted by mining activities, was used to evaluate (i) the influence of the co-contaminants present on the predicted speciation, and (ii) the influence of using nine different model/database combinations on the predictions. Generally, co-contaminants were found to have no significant effects on speciation, with the exception of Fe(III) in one system, where formation of hydrous ferric oxide and adsorption of uranyl to its surface impacted the predicted speciation. Model and database choice on the other hand clearly influenced speciation prediction. Complexes with dissolved organic matter, which could be simulated by three of the nine model/database combinations, were predicted to be important in a slightly acidic, soft water. Model prediction of uranyl and thorium speciation needs to take account of database comprehensiveness and cohesiveness, including the capability of the model and database to simulate interactions with dissolved organic matter. Measurement of speciation in natural waters is needed to provide data that may be used to assess and improve model capabilities and to better constrain the type of predictive modelling work presented here.

Fractions affected and probabilistic risk assessment of Cu, Zn, Cd, and Pb in soils using the free ion approach.

The free ion approach quantifies the toxic effects of cationic metals on soil biota as a function of chemistry. The approach is here extended to calculate the general relationship among toxic effects as the Fraction Affected (FA), soil solution pH, and soil organic matter content (SOM) for Cu, Zn, Cd, and Pb within toxicity data sets from literature. The dependence of FA on SOM is strong, with the FA decreasing as the SOM increases. The dependence of FA upon pH varies; Cd and Zn show strong dependences while for Cu and Pb dependences are weaker. The FA usually decreases as the soil pH increases. When the free ion approach is applied, risks across soils due to different metals can be compared using the FA. The free ion approach may also be applied to probabilistic risk assessment. Risk values, using the joint probability curve approach, were derived for Pb using two field soil data sets. One data set, with higher SOM than that of the Pb toxicity data set, gave a lower risk with the free ion approach than that when the soil chemistry was not considered. The other data set had lower SOM than that of the toxicity data set and gave a higher risk with the free ion approach. Since literature toxicity tests are biased toward low SOM soils of circumneutral pH, using such data to perform classical risk assessment for soils of differing chemical composition can lead to misestimation of risk due to neglecting soil chemistry, especially in soils with extreme pH and/or SOM.