Pluvial flood risk assessment for 2021-2050 under climate change scenarios in the Metropolitan City of Venice

Pluvial flood is a natural hazard occurring from extreme rainfall events that affect millions of people around the world, causing damages to their properties and lives. The magnitude of projected climate risks indicates the urgency of putting in place actions to increase climate resilience. Through this study, we develop a Machine Learning (ML) model to predict pluvial flood risk under Representative Concentration Pathways (RCP) 4.5 and 8.5 for future scenarios of precipitation for the period 2021-2050, considering different triggering factors and precipitation patterns. The analysis is focused on the case study area of the Metropolitan City of Venice (MCV) and considers 212 historical pluvial flood events occurred in the timeframe 1995-2020. The methodology developed implements spatio-temporal constraints in the ML model to improve pluvial flood risk prediction under future scenarios of climate change. Accordingly, a cross-validation approach was applied to frame a model able to predict pluvial flood at any time and space. This was complemented with historical pluvial flood data and the selection of nine triggering factors representative of territorial features that contribute to pluvial flood events. Logistic Regression was the most reliable model, with the highest AUC score, providing robust result both in the validation and test set. Maximum cumulative rainfall of 14 days was the most important feature contributing to pluvial flood occurrence. The final output is represented by a suite of risk maps of the flood-prone areas in the MCV for each quarter of the year for the period 1995-2020 based on historical data, and risk maps for each quarter of the period 2021-2050 under RCP4.5 and 8.5 of future precipitation scenarios. Overall, the results underline a consistent increase in extreme events (i.e., very high and extremely high risk of pluvial flooding) under the more catastrophic scenario RCP8.5 for future decades compared to the baseline.

Environmental management cycles for chemicals and climate change, EMC4 : A new conceptual framework contextualizing climate and chemical risk assessment and management

The environmental management cycles for chemicals and climate change (EMC4 ) is a suggested conceptual framework for integrating climate change aspects into chemical risk management. The interaction of climate change and chemical risk brings together complex systems that are imperfectly understood by science. Making management decisions in this context is therefore difficult and often exacerbated by a lack of data. The consequences of poor decision-making can be significant for both environmental and human health. This article reflects on the ways in which existing chemicals management systems consider climate change and proposes the EMC4 conceptual framework, which is a tool for decision-makers operating at different spatial scales. Also presented are key questions raised by the tool to help the decision-maker identify chemical risks from climate change, management options, and, importantly, the different types of actors that are instrumental in managing that risk. Case studies showing decision-making at different spatial scales are also presented highlighting the conceptual framework's applicability to multiple scales. The United Nations Environment Programme's development of an intergovernmental Science Policy Panel on Chemicals and Waste has presented an opportunity to promote and generate research highlighting the impacts of chemicals and climate change interlinkages. Integr Environ Assess Manag 2024;20:433-453. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Multi-model chain for climate change scenario analysis to support coastal erosion and water quality risk management for the Metropolitan city of Venice

Under the influence of anthropogenic climate change, hazardous climate and weather events are increasing in frequency and severity, with wide-ranging impacts across ecosystems and landscapes, especially fragile and dynamic coastal zones. The presented multi-model chain approach combines ocean hydrodynamics, wave fields, and shoreline extraction models to build a Bayesian Network-based coastal risk assessment model for the future analysis of shoreline evolution and seawater quality (i.e., suspended particulate matter, diffuse attenuation of light). In particular, the model was designed around a baseline scenario exploiting historical shoreline and oceanographic data within the 2015-2017 timeframe. Shoreline erosion and water quality changes along the coastal area of the Metropolitan city of Venice were evaluated for 2021-2050, under the RCP8.5 future scenario. The results showed a destabilizing trend in both shoreline evolution and seawater quality under the selected climate change scenario. Specifically, after a stable period (2021-2030), the shoreline will be affected by periods of erosion (2031-2040) and then accretion (2041-2050), with a simultaneous decrease in seawater quality in terms of higher turbidity. The decadal analysis and sensitivity evaluation of the input variables demonstrates a strong influence of oceanographic variables on the assessed endpoints, highlighting how the factors are strongly connected. The integration of regional and global climate models with Machine Learning and satellite imagery within the proposed multi-model chain represents an innovative update on state-of-the-art techniques. The validated outputs represent a good promise for better understanding the varying impacts due to future climate change conditions (e.g., wind, wave, tide, and sea-level). Moreover, the flexibility of the approach allows for the quick integration of climate and multi-risk data as it becomes available, and would represent a useful tool for forward-looking coastal risk management for decision-makers.

Framework for multirisk climate scenarios across system receptors with application to the Metropolitan City of Venice

Climate change influences the frequency of extreme events that affect both human and natural systems. It requires systemic climate change adaptation to address the complexity of risks across multiple domains and tackle the uncertainties of future scenarios. This paper introduces a multirisk analysis of climate hazard, exposure, vulnerability, and risk severity, specifically designed to hotspot geographic locations and prioritize system receptors that are affected by climate-related extremes. The analysis is demonstrated for the Metropolitan City of Venice. Representative scenarios (RCP4.5 and RCP8.5) of climate threats (i.e., storm surges, pluvial flood, heat waves, and drought) are selected and represented by projections of Regional Climate Models for a 30-year period (2021-2050). A sample of results is as follows. First, an increase in the risk is largely due to drought, pluvial flood, and storm surge, depending on the areas of interest, with the overall situation worsening under the RCP8.5 scenario. Second, particular locations have colocated vulnerable receptors at higher risk, concentrated in the urban centers (e.g., housing, railways, roads) and along the coast (e.g., beaches, wetlands, primary sector). Third, risk communication of potential environmental and socio-economic losses via the multirisk maps is useful to stakeholders and public administration. Fourth, the multirisk maps recommend priorities for future investigation and risk management, such as collection of sensor data, elaboration of mitigations, and adaptation plans at hotspot locations.

Evaluating the combined effect of climate and anthropogenic stressors on marine coastal ecosystems: Insights from a systematic review of cumulative impact assessment approaches

Cumulative impacts increasingly threaten marine and coastal ecosystems. To address this issue, the research community has invested efforts on designing and testing different methodological approaches and tools that apply cumulative impact appraisal schemes for a sound evaluation of the complex interactions and dynamics among multiple pressures affecting marine and coastal ecosystems. Through an iterative scientometric and systematic literature review, this paper provides the state of the art of cumulative impact assessment approaches and applications. It gives a specific attention to cutting-edge approaches that explore and model inter-relations among climatic and anthropogenic pressures, vulnerability and resilience of marine and coastal ecosystems to these pressures, and the resulting changes in ecosystem services flow. Despite recent advances in computer sciences and the rising availability of big data for environmental monitoring and management, this literature review evidenced that the implementation of advanced complex system methods for cumulative risk assessment remains limited. Moreover, experts have only recently started integrating ecosystem services flow into cumulative impact appraisal frameworks, but more as a general assessment endpoint within the overall evaluation process (e.g. changes in the bundle of ecosystem services against cumulative impacts). The review also highlights a lack of integrated approaches and complex tools able to frame, explain, and model spatio-temporal dynamics of marine and coastal ecosystems' response to multiple pressures, as required under relevant EU legislation (e.g., Water Framework and Marine Strategy Framework Directives). Progress in understanding cumulative impacts, exploiting the functionalities of more sophisticated machine learning-based approaches (e.g., big data integration), will support decision-makers in the achievement of environmental and sustainability objectives.

Combining remote sensing analysis with machine learning to evaluate short-term coastal evolution trend in the shoreline of Venice

With increasing storminess and incessant sea-level rise, coastal erosion is becoming a primary issue along many littorals in the world. To cope with present and future climate change scenarios, it is important to map the shoreline position over years and assess the coastal erosion trends to select the best risk management solutions and guarantee a sustainable management of communities, structures, and ecosystems. However, this objective is particularly challenging on gentle-sloping sandy coasts, where also small sea-level changes trigger significant morphological evolutions. This study presents a multidisciplinary study combining satellite images with Machine Learning and GIS-based spatial tools to analyze short-term shoreline evolution trends and detect erosion hot-spots on the Venice coast over the period 2015-2019. Firstly, advanced image preprocessing, which is not frequently adopted in coastal erosion studies, was performed on satellite images downloaded within the same tidal range. Secondly, different Machine Learning classification methods were tested to accurately define shoreline position by recognizing the land-sea interface in each image. Finally, the application of the Digital Shoreline Analysis System tool was performed to evaluate and visualize coastal changes over the years. Overall, the case study littoral reveals to be stable or mainly subjected to accretion. This is probably due to the high presence of coastal protection structures that stabilize the beaches, enhancing deposition processes. In detail, with respect to the total length of the considered shoreline (about 83 km), 5 % of the coast is eroding, 36 % is stable, 52 % is accreting and 7 % is not evaluable. Despite a significant coastal erosion risk was not recognized within this region, well-delimited erosion hot-spots were mapped in correspondence of Caorle, Jesolo and Cavallino-Treporti municipalities. These areas deserve higher attention for territorial planning and prioritization of adaptation measures, facing climate change scenarios and sea-level rise emergencies in the context of Integrated Coastal Zone Management.

Ecosystem services at risk in Italy from coastal inundation under extreme sea level scenarios up to 2050: A spatially resolved approach supporting climate change adaptation

According to the latest projections of the Intergovernmental Panel on Climate Change, at the end of the century, coastal zones and low-lying ecosystems will be increasingly threatened by rising global mean sea levels. In order to support integrated coastal zone management and advance the basic "source-pathway-receptor-consequence" approach focused on traditional receptors (e.g., population, infrastructure, and economy), a novel risk framework is proposed able to evaluate potential risks of loss or degradation of ecosystem services (ESs) due to projected extreme sea level scenarios in the Italian coast. Three risk scenarios for the reference period (1969-2010) and future time frame up to 2050 under RCP4.5 and RCP8.5 are developed by integrating extreme water-level projections related to changing climate conditions, with vulnerability information about the topography, distance from coastlines, and presence of artificial protections. A risk assessment is then performed considering the potential effects of the spatial-temporal variability of inundations and land use on the supply level and spatial distribution of ESs. The results of the analysis are summarized into a spatially explicit risk index, useful to rank coastal areas more prone to ESs losses or degradation due to coastal inundation at the national scale. Overall, the Northern Adriatic coast is scored at high risk of ESs loss or degradation in the future scenario. Other small coastal strips with medium risk scores are the Eastern Puglia coast, Western Sardinia, and Tuscany's coast. The ESs Coastal Risk Index provides an easy-to-understand screening assessment that could support the prioritization of areas for coastal adaptation at the national scale. Moreover, this index allows the direct evaluation of the public value of ecosystems and supports more effective territorial planning and environmental management decisions. In particular, it could support the mainstreaming of ecosystem-based approaches (e.g., ecological engineering and green infrastructures) to mitigate the risks of climate change and extreme events while protecting ecosystems and biodiversity. Integr Environ Assess Manag 2022;18:1564-1577. © 2021 SETAC.

Prioritization of Resilience Initiatives for Climate-Related Disasters in the Metropolitan City of Venice

Increases in the magnitude and frequency of climate and other disruptive factors are placing environmental, economic, and social stresses on coastal systems. This is further exacerbated by land use transformations, urbanization, over-tourism, sociopolitical tensions, technological innovations, among others. A scenario-informed multicriteria decision analysis (MCDA) was applied in the Metropolitan City of Venice integrating qualitative (i.e., local stakeholder preferences) and quantitative information (i.e., climate-change projections) with the aim of enhancing system resilience to multiple climate-related threats. As part of this analysis, different groups of local stakeholders (e.g., local authorities, civil protection agencies, SMEs, NGOs) were asked to identify critical functions that needs to be sustained. Various policy initiatives were considered to support these critical functions. The MCDA was used to rank the initiatives across several scenarios describing main climate threats (e.g., storm surges, floods, heatwaves, drought). We found that many climate change scenarios were considered to be disruptive to stakeholders and influence alternative ranking. The management alternatives acting on physical domain generally enhance resilience across just a few scenarios while cognitive and informative initiatives provided resilience enhancement across most scenarios considered. With uncertainty of multiple stressors along with projected climate variability, a portfolio of cognitive and physical initiatives is recommended to enhance resilience.

Development of a Multi-Dimensional Coastal Vulnerability Index: Assessing vulnerability to inundation scenarios in the Italian coast

Understanding how natural and human-induced drivers will contribute to rising vulnerability and risks in coastal areas requires a broader use of future projections capturing the spatio-temporal dynamics which drive changes in the different vulnerability dimensions, including the socio-demographic and economic spheres. To go beyond the traditional approaches for coastal vulnerability appraisal, a Multi-dimensional Coastal Vulnerability Index (MDim-CVI) - integrating a composite set of physical, environmental and socio-economic indicators - is proposed to rank Italian coastal provinces according to their relative vulnerability to extreme sea level scenarios, in 2050. Specifically, information on hazard-prone areas, potentially inundated by sea level rise and extreme water levels (under the RCP8.5 climate scenario) is combined with indicators of geomorphic vulnerability (e.g. elevation, distance from coastline, shoreline evolution trend) exposure, and adaptive capacity (e.g. sensible segments of the population, GDP, land use patterns). The methodology is applied to a reference timeframe, representing current climate and land use condition, and a future scenario for the year 2050, integrating both climate projections and data simulating potential evolution of the environmental and socio-economic systems. Results show that most vulnerable provinces are located in the North Adriatic, the Gargano area and other Southern parts of Italy, mostly due to the very high vulnerability scores reported by climate-related indicators (e.g. extreme sea level). The number of vulnerable provinces as well as the magnitude of vulnerability is expected to increase in the future due to the worsening of climate, environmental, and socio-economic conditions (e.g. land use variations and increase of the elderly population). These outcomes can timely inform integrated coastal zone management and support climate adaptation planning.

Higher Fine Particle Fraction in Sediment Increased Phosphorus Flux to Estuary in Restored Yellow River Basin

River delta-front estuaries (DEs) are vital interfaces for fluxes between terrestrial and marine environments. However, deep uncertainty exists in estimating the sedimentary pollutant flux from terrestrial environments in DEs due, in part, to a lack of direct measurements in these dynamic and complicated regions and uncertainty in the calculation method. Due to its high sediment content, the Yellow River (YR) has a strong ability to adsorb phosphorus; therefore, it reliably reflects estuarine sedimentary processes. Here, through the comprehensive analysis of field samples, monitoring data and remote sensing images, we conclude that riverine fine particles control the deltaic estuary pollution status and that particle size is the key factor. Based on the stable relationships between phosphorus and heavy metals, with r² values of 0.990, 0.992, and 0.639 for As, Cd, and Cr, respectively, we estimated that the P flux reached 22.68 g/m² yr in 2017. Analysis of the YR high-silt sediment load, which has a strong phosphorus adsorption ability and constitutes a substantial fraction of global fluvial sediment transport, revealed a negative correlation between the riverine sediment load and the estuarine phosphorus flux.

Egypt's Coastal Vulnerability to Sea-Level Rise and Storm Surge: Present and Future Conditions

We assess the relative vulnerability of the Mediterranean shoreline of Egypt (about 1000 km in length) to climate change (i.e., sea-level rise [SLR], storm surge flooding, and coastal erosion) by using a Climate-improved Coastal Vulnerability Index (CCVI). We integrate information relative to a multidimensional set of physical, geological, and socioeconomic variables, and add to the mainstream literature the consideration of both a reference and a climate change scenario, assuming the representative concentration pathway 8.5 W/m² (RCP8.5) for the 21st century in the Mediterranean region. Results report that approximately 1% (~43 km²) of the mapped shoreline is classifiable as having a high or very high vulnerability, whereas approximately 80% (4652 km²) shows very low vulnerability. As expected, exposure to inundation and erosion is especially relevant in highly developed and urbanized coastal areas. Along the shoreline, while the Nile Delta region is the most prone area to coastal erosion and permanent or occasional inundations (both in the reference and in the climate scenario), results show the Western Desert area to be less vulnerable due to its geological characteristics (i.e., rocky and cliffed coasts, steeper coastal slope). The application of the CCVI to the coast of Egypt can be considered as a first screening of the hot-spot risk areas at the national scale. The results of the analysis, including vulnerability maps and indicators, can be used to support the development of climate adaptation and integrated coastal zone management strategies. Integr Environ Assess Manag 2020;16:761-772. © 2020 SETAC.

Multi-scenario analysis in the Adriatic Sea: A GIS-based Bayesian network to support maritime spatial planning

Oceans are changing faster than even observed before. Unprecedented climate variability is interacting with long-term trends, all against a backdrop of rising anthropogenic use of marine space. The growth of maritime activities is taking place without the full understanding of complex interactions between natural and human-induced changes, leading to a progressive decline of biodiversity and degradation of marine ecosystems. Against this complex interplay, marine managers and policy makers are increasingly calling for new approaches and tools allowing a multi-scenario assessment of environmental impacts arising from the complex interaction between natural and anthropogenic drivers, also in consideration of multiple marine plans objectives. Responding to this need, for the Adriatic Sea we developed a GIS-based Bayesian Network to evaluate the probability (and related uncertainty) of cumulative impacts under four 'what-if' scenarios representing different marine management options and climate conditions. We addressed issues concerning consequences of potential planning measures, as well as management programmes required to achieve environmental status targets, as required by relevant EU acquis. Results from the scenario analysis highlighted that an integrated approach to maritime spatial planning is required, combining more sustainable management options of marine spaces and resources with climate adaptation strategies. This approach to planning would allow to reduce human pressures on the marine environment and rise resilience of natural ecosystems to climate and human-induced disturbances, which would result in an overall decrease of cumulative impacts.

Cumulative Impact Index for the Adriatic Sea: Accounting for interactions among climate and anthropogenic pressures

Assessing and managing cumulative impacts produced by interactive anthropogenic and natural drivers is a major challenge to achieve the sustainable use of marine spaces in line with the objectives of relevant EU acquis. However, the complexity of the marine environment and the uncertainty linked to future climate and socio-economic scenarios, represent major obstacles for understanding the multiplicity of impacts on the marine ecosystems and to identify appropriate management strategies to be implemented. Going beyond the traditional additive approach for cumulative impact appraisal, the Cumulative Impact Index (CI-Index) proposed in this paper applies advanced Multi-Criteria Decision Analysis techniques to spatially model relationships between interactive climate and anthropogenic pressures, the environmental exposure and vulnerability patterns and the potential cumulative impacts for the marine ecosystems at risk. The assessment was performed based on spatial data characterizing location and vulnerability of 5 relevant marine targets (e.g. seagrasses and coral beds), and the distribution of 17 human activities (e.g. trawling, maritime traffic) during a reference scenario 2000-2015. Moreover, projections for selected physical and biogeochemical parameters (temperature and chlorophyll 'a') for the 2035-2050 timeframe under RCP8.5 scenario, were integrated in the assessment to evaluate index variations due to changing climate conditions. The application of the CI-Index in the Adriatic Sea, showed higher cumulative impacts in the Northern part of the basin and along the Italian continental shelf, where the high concentration of human activities, the seawater temperature conditions and the presence of vulnerable benthic habitats, contribute to increase the overall impact estimate. Moreover, the CI-Index allowed understanding which are the phenomena contributing to synergic pressures creating potential pathways of environmental disturbance for marine ecosystems. Finally, the application in the Adriatic case showed how the output of the CI-Index can provide support to evaluate multi-risk scenarios and to drive sustainable maritime spatial planning and management.

Alteration of freshwater ecosystem services under global change - A review focusing on the Po River basin (Italy) and the Red River basin (Vietnam)

Freshwater ecosystem services are negatively affected by factors such as climate change (e.g. changes in temperature, precipitation, and sea level rise) and human interventions (e.g. agriculture practices, impoundment of dams, and land use/land cover change). Moreover, the potential synergic impacts of these factors on ecosystems are unevenly distributed, depending on geographical, climatic and socio-economic conditions. The paper aims to review the complex effects of climatic and non-climatic drivers on the supply and demand of freshwater ecosystem services. Based on the literature, we proposed a conceptual framework and a set of indicators for assessing the above-mentioned impacts due to global change, i.e. climate change and human activities. Then, we checked their applicability to the provisioning services of two well-known case studies, namely the Po River basin (Italy) and the Red River basin (Vietnam). To define the framework and the indicators, we selected the most relevant papers and reports; identified the major drivers and the most relevant services; and finally summarized the fundamental effects of these drivers on those services. We concluded that the proposed framework was applicable to the analyzed case studies, but it was not straightforward to consider all the indicators since ecosystem services were not explicitly considered as key assessment endpoints in these areas. Additionally, the supply of ecosystem services was found to draw much more attention than their demand. Finally, we highlighted the importance of defining a common and consistent terminology and classification of drivers, services, and effects to reduce mismatches among ecosystem services when conducting a risk assessment.

Multi-risk assessment in mountain regions: A review of modelling approaches for climate change adaptation

Climate change has already led to a wide range of impacts on our society, the economy and the environment. According to future scenarios, mountain regions are highly vulnerable to climate impacts, including changes in the water cycle (e.g. rainfall extremes, melting of glaciers, river runoff), loss of biodiversity and ecosystems services, damages to local economy (drinking water supply, hydropower generation, agricultural suitability) and human safety (risks of natural hazards). This is due to their exposure to recent climate warming (e.g. temperature regime changes, thawing of permafrost) and the high degree of specialization of both natural and human systems (e.g. mountain species, valley population density, tourism-based economy). These characteristics call for the application of risk assessment methodologies able to describe the complex interactions among multiple hazards, biophysical and socio-economic systems, towards climate change adaptation. Current approaches used to assess climate change risks often address individual risks separately and do not fulfil a comprehensive representation of cumulative effects associated to different hazards (i.e. compound events). Moreover, pioneering multi-layer single risk assessment (i.e. overlapping of single-risk assessments addressing different hazards) is still widely used, causing misleading evaluations of multi-risk processes. This raises key questions about the distinctive features of multi-risk assessments and the available tools and methods to address them. Here we present a review of five cutting-edge modelling approaches (Bayesian networks, agent-based models, system dynamic models, event and fault trees, and hybrid models), exploring their potential applications for multi-risk assessment and climate change adaptation in mountain regions. The comparative analysis sheds light on advantages and limitations of each approach, providing a roadmap for methodological and technical implementation of multi-risk assessment according to distinguished criteria (e.g. spatial and temporal dynamics, uncertainty management, cross-sectoral assessment, adaptation measures integration, data required and level of complexity). The results show limited applications of the selected methodologies in addressing the climate and risks challenge in mountain environments. In particular, system dynamic and hybrid models demonstrate higher potential for further applications to represent climate change effects on multi-risk processes for an effective implementation of climate adaptation strategies.

"AMORE" Decision Support System for probabilistic Ecological Risk Assessment - Part II: Effect assessment of the case study on cyanide

Ecotoxicological data are highly important for risk assessment processes and are used for deriving environmental quality criteria, which are enacted for assuring the good quality of waters, soils or sediments and achieving desirable environmental quality objectives. Therefore, it is of significant importance the evaluation of the reliability and relevance of available data for analysing their possible use in the aforementioned processes. In this context, a new methodology which has been developed based on Multi-Criteria Decision Analysis (MCDA) techniques, is being used, demonstrated and tested for analysing the reliability and relevance of ecotoxicological data of cyanide (which are produced through laboratory biotests for individual effects). The proposed methodology is also used for the production of Weighted by Data Quality Species Sensitivity Distributions (SSD-WDQ), as a component of the Ecological Risk Assessment of chemicals in aquatic systems. The SSD-WDQ production resulted in the estimation of environmental quality criteria (hazard concentration affecting 5% and 50% of the species). The proposed work is part of the development of the AMORE Decision Support System (DSS) for the application of probabilistic Ecological Risk Assessment (ERA), presented in the companion paper (Isigonis et al., 2019). The DSS has been tested through a case study on ERA of cyanide in the watershed of river Selune in France. The paper presents the 'Effect Assessment' of cyanide, based on the aforementioned methodologies. The main results presented in the paper are the probabilistic analysis of the estimated species sensitivity on cyanide (Effect Assessment) and the calculation of Hazardous Concentration (HCx) of the same contaminant in the Selune river area, based on the functionalities of the DSS. The results are described and discussed in detail, with the use of various graphs and indices. The indices are calculated for all the available ecotoxicological data, as well as for the data on trophic levels or taxonomic groups separately. An effect comparison is presented between the innovative methodologies included in the DSS and the currently existing methodologies.

"AMORE" Decision Support System for probabilistic Ecological Risk Assessment - Part I: Exposure and risk assessment of the case study on cyanide

Ecological Risk Assessment of chemicals in fluvial systems is a highly researched topic, but its importance for the environmental protection of our planet is vital. Thus, new developments and improvements to existing methodologies are proposed constantly, for providing more advanced tools and more accurate results to researchers and other interested parties. In the field of probabilistic Ecological Risk Assessment, a new Decision Support System is proposed, developed, tested and evaluated. The AMORE DSS is a modular DSS, which incorporates a series of new methodologies, and is built upon the notions of 'Exposure Assessment', 'Effect Assessment' and 'Risk Assessment'. The AMORE Decision Support System has been developed as part of the AMORE research project (French National Research Agency project). The DSS provides a set of tools for analysing and integrating both exposure and effect information in order to evaluate the risk for species living on a given contaminated aquatic system in terms of the Potentially Affected Fraction. The DSS has been tested through a case study on ERA of cyanide in the watershed of river Selune in France. The paper presents the 'Exposure Assessment' and 'Risk Assessment' of the cyanide case study, as well as the complete functionalities of the AMORE DSS. The main results presented in the paper are the statistical analysis of the measured environmental concentrations of cyanide (Exposure Assessment) and the probabilistic 'Risk assessment' of the same contaminant in the area of interest, based on the functionalities of the DSS. The results are described and discussed in detail with the use of various graphs and risk indices. The risk indices are calculated for all the available ecotoxicological data, as well as for the data on trophic levels or taxonomic groups separately. A risk comparison is presented between the innovative methodologies included in the DSS and the currently existing methodologies.

Bioaccumulation of trace metals in aquatic food web. A case study, Liaodong Bay, NE China

The recently developed modelling tool MERLIN-Expo was applied to support the exposure assessment of an aquatic food web to trace metals in a coastal environment. The exposure scenario, built on the data from Daliao River estuary in the Liaodong Bay (Bohai Sea, China), affected by long-term and large-scale industrial activities as well as rapid urbanization in Liao River watershed, represents an interesting case-study for ecological exposure modelling due to the availability of local data on metal concentrations in water and sediment. The bioaccumulation of selected trace metals in aquatic organisms was modelled and compared with field data from local aquatic organisms. Both model results and experimental data demonstrated that As, Cd, Cu, Ni, Pb and Zn, out of examined metals, were accumulated most abundantly by invertebrates and less by higher trophic level species. The body parts of the sampled animals with the highest measured concentration of metals were predominantly muscles, intestine and liver and fish skin in the case of Cr. The Morris and extended Fourier Analysis (EFAST) were used to account for variability in selected parameters of the bioaccumulation model. Food assimilation efficiency and slopes and intercepts of two sub-models for calculating metal specific BCFs (BCF_metal-exposure concentration) and fish weight (Weight_fish-Length_fish) were identified as the most influential parameters on ecological exposure to selected metals.

The Temporal and Spatial Distribution Characteristics of Air Pollution Index and Meteorological Elements in Beijing, Tianjin, and Shijiazhuang, China

With rapid economic development and continuous population growth, several important cities in China suffer serious air pollution, especially in the Beijing-Tianjin-Hebei economic developing area. Based on the daily air pollution index (API) and surface meteorological elements in Beijing, Tianjin, and Shijiazhuang (the capital of Hebei province) from 2001 to 2010, the relationships between API and meteorological elements were analyzed. The statistical analysis focused on the relationships at seasonal and monthly average scales, on different air pollution grades and air pollution processes. The results revealed that the air pollution conditions in the 3 areas gradually improved from 2001 to 2010, especially during summer; the worst conditions in air quality were recorded in Beijing in spring due to the influences of dust, and in Tianjin and Shijiazhuang in winter due to household heating. Meteorological elements exhibited different influences on air pollution, showing similar relationships between API in monthly averages and 4 meteorological elements (i.e., the average, maximum, and minimum temperatures; maximum air pressure; vapor pressure; and maximum wind speed), whereas the relationships on a seasonal average scale demonstrated significant differences. Compared with seasonal and monthly average scales of API, the relation coefficients based on different air pollution grades were significantly lower, whereas the relationship between API and meteorological elements based on air pollution processes reduced the smoothing effect due to the average processing of seasonal and monthly API and improved the accuracy of the results. Finally, statistical analysis of the distribution of pollution days in different wind directions indicated the directions of extreme and maximum wind speeds that mainly influence air pollution, representing valuable information that could support the definition of air pollution control strategies through the identification of the regions (and the located emission sources) where the implementation of emission reduction actions should be focused. Integr Environ Assess Manag 2018;14:710-721. © 2018 SETAC.

An Information System for Brownfield Regeneration: providing customised information according to stakeholders' characteristics and needs

In the EU brownfield presence is still considered a widespread problem. Even though, in the last decades, many research projects and initiatives developed a wealth of methods, guidelines, tools and technologies aimed at supporting brownfield regeneration. However, this variety of products had and still has a limited practical impact on brownfield revitalisation success, because they are not used in their entire potential due to their scarce visibility. Also, another problem that stakeholders face is finding customised information. To overcome this non-visibility and not-sufficient customisation of information, the Information System for Brownfield Regeneration (ISBR) has been developed, based on Artificial Neural Networks, which allows understanding stakeholders' information needs by providing tailored information. The ISBR has been tested by stakeholders from the EU project TIMBRE case studies, located in the Czech Republic, Germany, Poland and Romania. Data gained during tests allowed to understand stakeholders' information needs. Overall, stakeholders showed to be concerned first on remediation aspects, then on benchmarking information, which are valuable to improve practices in the complex field of brownfield regeneration, and then on the relatively new issue of sustainability applied to brownfield regeneration and remediation. Mature markets confirmed their interest for remediation-related aspects, highlighting the central role that risk assessment plays in the process. Emerging markets showed to seek information and tools for strategic and planning issues, like brownfield inventories and georeferenced data sets. Results led to conclude that a new improved platform, combining the ISBR functionalities with geo-referenced ones, would be useful and could represent a further research application.

Modelling climate change impacts on nutrients and primary production in coastal waters

There is high confidence that the anthropogenic increase of atmospheric greenhouse gases (GHGs) is causing modifications in the Earth's climate. Coastal waterbodies such as estuaries, bays and lagoons are among those most affected by the ongoing changes in climate. Being located at the land-sea interface, such waterbodies are subjected to the combined changes in the physical-chemical processes of atmosphere, upstream land and coastal waters. Particularly, climate change is expected to alter phytoplankton communities by changing their environmental drivers (especially climate-related), thus exacerbating the symptoms of eutrophication events, such as hypoxia, harmful algal blooms (HAB) and loss of habitat. A better understanding of the links between climate-related drivers and phytoplankton is therefore necessary for projecting climate change impacts on aquatic ecosystems. Here we present the case study of the Zero river basin in Italy, one of the main contributors of freshwater and nutrient to the salt-marsh Palude di Cona, a coastal waterbody belonging to the lagoon of Venice. To project the impacts of climate change on freshwater inputs, nutrient loadings and their effects on the phytoplankton community of the receiving waterbody, we formulated and applied an integrated modelling approach made of: climate simulations derived by coupling a General Circulation Model (GCM) and a Regional Climate Model (RCM) under alternative emission scenarios, the hydrological model Soil and Water Assessment Tool (SWAT) and the ecological model AQUATOX. Climate projections point out an increase of precipitations in the winter period and a decrease in the summer months, while temperature shows a significant increase over the whole year. Water discharge and nutrient loads simulated by SWAT show a tendency to increase (decrease) in the winter (summer) period. AQUATOX projects changes in the concentration of nutrients in the salt-marsh Palude di Cona, and variations in the biomass and species of the phytoplankton community.

Cadmium exposure and early renal effects in the children and adults living in a tungsten-molybdenum mining areas of South China

Adverse health effects related to accumulative cadmium (Cd) exposure have aroused widespread attention from the public in China. Knowledge on the relationships between Cd exposure and early renal effects is particularly limited for children, who are more susceptible to absorbing metals than adults. A typical Cd-polluted area of South China was selected to determine the Cd exposure and related early renal effects of the general population, including children. In total, 211 children and 806 adults were enrolled in the study. The urinary levels of Cd (U-Cd), β₂-microglobulin (U-BMG), retinol binding protein (U-RBP), and N-acetyl-β-D-glucosaminidase (U-NAG) were measured. The relationship between U-Cd and ranked indicators of early renal effects was examined by multiple regression analysis. The average U-Cd ranged from 7.01 μg/g creatinine (boys) to 13.55 μg/g creatinine (women) in the Cd-polluted areas. These values are much higher than those of the control group and those that have been reported by other countries. In agreement with previous studies, environmental Cd pollution resulted in elevated Cd accumulation in the bodies of children, and it increased the concentration of NAG in their urine. Similarly, environmental Cd pollution increased NAG and BMG in the urine of adults. Multivariate models showed that the urinary excretion of BMG, RBP, and NAG was positively associated with Cd levels in the urine of both children and adults. The reference thresholds of U-Cd in relation to elevated U-BMG, U-RBP, and U-NAG were higher in children than adults after standardization for other covariates. These results reinforce the need to control and regulate the sources of environmental Cd contamination and to promote more effective risk management measures, especially for vulnerable groups.

Spatially explicit risk approach for multi-hazard assessment and management in marine environment: The case study of the Adriatic Sea

In the last few decades the health of marine ecosystems has been progressively endangered by the anthropogenic presence. Natural and human-made pressures, as well as climate change effects, are posing increasing threats on marine areas, triggering alteration of biological, chemical and physical processes. Planning of marine areas has become a challenge for decision makers involved in the design of sustainable management options. In order to address threats posed by climate drivers in combination with local to regional anthropogenic pressures affecting marine ecosystems and activities, a multi-hazard assessment methodology was developed and applied to the Adriatic Sea for the reference scenario 2000-2015. Through a four-stages process based on the consecutive analysis of hazard, exposure, vulnerability and risk the methodology allows a semi-quantitative evaluation of the relative risk from anthropogenic and natural sources to multiple endpoints, thus supporting the identification and ranking of areas and targets more likely to be at risk. Resulting output showed that the higher relative hazard scores are linked to exogenic pressures (e.g. sea surface temperature variation) while the lower ones resulted from endogenic and more localized stressors (e.g. abrasion, nutrient input). Relatively very high scores were observed for vulnerability over the whole case study for almost all the considered pressures, showing seagrasses meadows, maërl and coral beds as the most susceptible targets. The approach outlined in this study provides planners and decision makers a quick-screening tool to evaluate progress towards attaining a good environmental status and to identify marine areas where management actions and adaptation strategies would be best targeted. Moreover, by focusing on risks induced by land-based drivers, resulting output can support the design of infrastructures for reducing pressures on the sea, contributing to improve the land-sea interface management.

Reviewing Bayesian Networks potentials for climate change impacts assessment and management: A multi-risk perspective

The evaluation and management of climate change impacts on natural and human systems required the adoption of a multi-risk perspective in which the effect of multiple stressors, processes and interconnections are simultaneously modelled. Despite Bayesian Networks (BNs) are popular integrated modelling tools to deal with uncertain and complex domains, their application in the context of climate change still represent a limited explored field. The paper, drawing on the review of existing applications in the field of environmental management, discusses the potential and limitation of applying BNs to improve current climate change risk assessment procedures. Main potentials include the advantage to consider multiple stressors and endpoints in the same framework, their flexibility in dealing and communicate with the uncertainty of climate projections and the opportunity to perform scenario analysis. Some limitations (i.e. representation of temporal and spatial dynamics, quantitative validation), however, should be overcome to boost BNs use in climate change impacts assessment and management.

Probabilistic modeling of aggregate lead exposure in children of urban China using an adapted IEUBK model

Lead, a ubiquitous pollutant throughout the environment, is confirmed to be neurotoxic for children by pulmonary and oral routes. As preschool children in China continue to be exposed to lead, we analyzed the available biomonitoring data for preschool children in urban China collected in the period 2004-2014 through a literature review. To identify apportionment of lead exposure sources for urban children in China, we modified the IEUBK model with a Monte Carlo module to assess the uncertainty and variability of the model output based on limited available exposure data and compared the simulated blood lead levels with the observed ones obtained through literature review. Although children's blood lead levels in urban China decreased statistically over time for the included studies, changes in blood lead levels in three economic zones and seven age groups except for two age-specific groups were no longer significant. The GM-predicted BLLs and the GM-observed BLLs agreed within 1μg/dL for all fourteen cities. The 95% CIs for the predicted GMs and the observed distribution (GM±GSD) overlapped substantially. These results demonstrated the plausibility of blood lead prediction provided by the adapted IEUBK model. Lead exposure estimates for diet, soil/dust, air, and drinking water were 12.01±6.27μg/day, 2.69±0.89μg/day, 0.20±0.15μg/day, and 0.029±0.012μg/day, respectively. These findings showed that the reduction of lead concentrations in grains and vegetables would be beneficial to limit the risk of dietary lead exposure for a large proportion of preschool children in urban China.

Improving optimization efficiency for the total pollutant load allocation in large two-dimensional water areas: Bohai Sea (China) case study

The total pollutant load allocation (TPLA) can be transformed into an optimization problem with regards to water quality constraints. The optimization calculation may become very time consuming when the number of water quality constraint equations is great. A Trial and Error Method (TEM) to remove the redundant points was first introduced through iterative calculations under structure and non-structure model grids. The TEM was applied for the TPLA in the Bohai Sea in China. The calculation time was reduced to about 2min under the condition that 103,433 model grids met the water quality standards. In the best case, the optimization efficiency was improved by 98.9%. The allocation results showed that approximately 90% of total nitrogen (TN) load should be reduced in the 56 pollution sources around the Bohai Sea; of these values, roughly 85% of the reduction could come from 10 pollution sources.

Comparison of international approaches to sustainable remediation

Since mid-to-late 2000s growing interest for sustainable remediation has emerged in initiatives from several international and national organisations as well as other initiatives from networks and forums. This reflects a realisation that risk-management activities can about bring environmental, social, and economic impacts (positive or negative) in addition to achieving risk-based remediation goals. These ideas have begun to develop as a new discipline of "sustainable remediation". The various initiatives have now published a number of frameworks, standards, white papers, road maps and operative guidelines. The similarities and differences in the approaches by these outputs and general trends have been identified. The comparison is based on a set of criteria developed in discussion with members of these various initiatives, and identifies a range of similarities between their publications. Overall the comparison demonstrates a high level of consensus across definitions and principles, which leads to the conclusion that there is a shared understanding of what sustainable remediation is both across countries and stakeholder groups. Publications do differ in points of detail, in particular about the operational aspects of sustainable remediation assessment. These differences likely result from differences in context and legal framework. As this analysis was carried out its findings were debated with members of the various international initiatives, many of whom have been included as authors. Hence the outcomes described in this paper can be seen as the result of a sort of multi-level debate among international experts (authors) and so can offer a starting point to new sustainable remediation initiatives (for example in other countries) that aim to start developing their own documents.

Targeted selection of brownfields from portfolios for sustainable regeneration: User experiences from five cases testing the Timbre Brownfield Prioritization Tool

Prioritizing brownfields for redevelopment in real estate portfolios can contribute to more sustainable regeneration and land management. Owners of large real estate and brownfield portfolios are challenged to allocate their limited resources to the development of the most critical or promising sites, in terms of time and cost efficiency. Authorities worried about the negative impacts of brownfields - in particular in the case of potential contamination - on the environment and society also need to prioritize their resources to those brownfields that most urgently deserve attention and intervention. Yet, numerous factors have to be considered for prioritizing actions, in particular when adhering to sustainability principles. Several multiple-criteria decision analysis (MCDA) approaches and tools have been suggested in order to support these actors in managing their brownfield portfolios. Based on lessons learned from the literature on success factors, sustainability assessment and MCDA approaches, researchers from a recent EU project have developed the web-based Timbre Brownfield Prioritization Tool (TBPT). It facilitates assessment and prioritization of a portfolio of sites on the basis of the probability of successful and sustainable regeneration or according to individually specified objectives. This paper introduces the challenges of brownfield portfolio management in general and reports about the application of the TBPT in five cases: practical test-uses by two large institutional land owners from Germany, a local and a regional administrative body from the Czech Republic, and an expert from a national environmental authority from Romania. Based on literature requirements for sustainability assessment tools and on the end-users' feedbacks from the practical tests, we discuss the TBPT's strengths and weaknesses in order to inform and give recommendations for future development of prioritization tools.

Modelling ecological and human exposure to POPs in Venice lagoon - Part II: Quantitative uncertainty and sensitivity analysis in coupled exposure models

The study is focused on applying uncertainty and sensitivity analysis to support the application and evaluation of large exposure models where a significant number of parameters and complex exposure scenarios might be involved. The recently developed MERLIN-Expo exposure modelling tool was applied to probabilistically assess the ecological and human exposure to PCB 126 and 2,3,7,8-TCDD in the Venice lagoon (Italy). The 'Phytoplankton', 'Aquatic Invertebrate', 'Fish', 'Human intake' and PBPK models available in MERLIN-Expo library were integrated to create a specific food web to dynamically simulate bioaccumulation in various aquatic species and in the human body over individual lifetimes from 1932 until 1998. MERLIN-Expo is a high tier exposure modelling tool allowing propagation of uncertainty on the model predictions through Monte Carlo simulation. Uncertainty in model output can be further apportioned between parameters by applying built-in sensitivity analysis tools. In this study, uncertainty has been extensively addressed in the distribution functions to describe the data input and the effect on model results by applying sensitivity analysis techniques (screening Morris method, regression analysis, and variance-based method EFAST). In the exposure scenario developed for the Lagoon of Venice, the concentrations of 2,3,7,8-TCDD and PCB 126 in human blood turned out to be mainly influenced by a combination of parameters (half-lives of the chemicals, body weight variability, lipid fraction, food assimilation efficiency), physiological processes (uptake/elimination rates), environmental exposure concentrations (sediment, water, food) and eating behaviours (amount of food eaten). In conclusion, this case study demonstrated feasibility of MERLIN-Expo to be successfully employed in integrated, high tier exposure assessment.

Modelling the exposure to chemicals for risk assessment: a comprehensive library of multimedia and PBPK models for integration, prediction, uncertainty and sensitivity analysis - the MERLIN-Expo tool

MERLIN-Expo is a library of models that was developed in the frame of the FP7 EU project 4FUN in order to provide an integrated assessment tool for state-of-the-art exposure assessment for environment, biota and humans, allowing the detection of scientific uncertainties at each step of the exposure process. This paper describes the main features of the MERLIN-Expo tool. The main challenges in exposure modelling that MERLIN-Expo has tackled are: (i) the integration of multimedia (MM) models simulating the fate of chemicals in environmental media, and of physiologically based pharmacokinetic (PBPK) models simulating the fate of chemicals in human body. MERLIN-Expo thus allows the determination of internal effective chemical concentrations; (ii) the incorporation of a set of functionalities for uncertainty/sensitivity analysis, from screening to variance-based approaches. The availability of such tools for uncertainty and sensitivity analysis aimed to facilitate the incorporation of such issues in future decision making; (iii) the integration of human and wildlife biota targets with common fate modelling in the environment. MERLIN-Expo is composed of a library of fate models dedicated to non biological receptor media (surface waters, soils, outdoor air), biological media of concern for humans (several cultivated crops, mammals, milk, fish), as well as wildlife biota (primary producers in rivers, invertebrates, fish) and humans. These models can be linked together to create flexible scenarios relevant for both human and wildlife biota exposure. Standardized documentation for each model and training material were prepared to support an accurate use of the tool by end-users. One of the objectives of the 4FUN project was also to increase the confidence in the applicability of the MERLIN-Expo tool through targeted realistic case studies. In particular, we aimed at demonstrating the feasibility of building complex realistic exposure scenarios and the accuracy of the modelling predictions through a comparison with actual measurements.

A multi-disciplinary approach to evaluate pluvial floods risk under changing climate: The case study of the municipality of Venice (Italy)

Global climate change is likely to pose increasing threats in nearly all sectors and across all sub-regions worldwide (IPCC, 2014). Particularly, extreme weather events (e.g. heavy precipitations), together with changing exposure and vulnerability patterns, are expected to increase the damaging effect of storms, pluvial floods and coastal flooding. Developing climate and adaptation services for local planners and decision makers is becoming essential to transfer and communicate sound scientific knowledge about climate related risks and foster the development of national, regional and local adaptation strategies. In order to analyze the effect of climate change on pluvial flood risk and advice adaptation planning, a Regional Risk Assessment (RRA) methodology was developed and applied to the urban territory of the municipality of Venice. Based on the integrated analysis of hazard, exposure, vulnerability and risk, RRA allows identifying and prioritizing targets and sub-areas that are more likely to be affected by pluvial flood risk due to heavy precipitation events in the future scenario 2041-2050. From the early stages of its development and application, the RRA followed a bottom-up approach taking into account the requests, knowledge and perspectives of local stakeholders of the North Adriatic region by means of interactive workshops, surveys and discussions. Results of the analysis showed that all targets (i.e. residential, commercial-industrial areas and infrastructures) are vulnerable to pluvial floods due to the high impermeability and low slope of the topography. The spatial pattern of risk mostly reflects the distribution of the hazard and the districts with the higher percentage of receptors' surface in the higher risk classes (i.e. very high, high and medium) are Lido-Pellestrina and Marghera. The paper discusses how risk-based maps and statistics integrate scientific and local knowledge with the final aim to mainstream climate adaptation in the development of risk mitigation and urban plans.

Contamination and human health risk of lead in soils around lead/zinc smelting areas in China

Pb/Zn smelting, an important economic activity in China, has led to heavy environmental pollution. This research reviewed studies on soil Pb contamination at Pb/Zn smelting sites in China published during the period of 2000 to 2015 to clarify the total levels, spatial changes, and health risks for Pb contamination in soils at local and national scales. The results show that Pb contents in surface soils at 58 Pb/Zn smelting sites in China ranged from 7 to 312,452 mg kg(-1) with an arithmetic average, geometric average, and median of 1982, 404, and 428 mg kg(-1), respectively (n = 1011). Surface soil Pb content at these smelting sites decreased from an average of 2466 to 659 mg kg(-1), then to 463 mg kg(-1) as the distance from the smelters increased from <1000 to 1000∼2000 m, and then to >2000 m. With respect to variation with depth, the average soil Pb content at these sites gradually decreased from 986 mg kg(-1) at 0- to 20-cm depth to 144 mg kg(-1) at 80- to 100-cm depth. Approximately 78 % of the soil samples (n = 1011) at the 58 Pb/Zn smelting sites were classified as having high Pb pollution levels. Approximately 34.2 and 7.7 % of the soil samples (n = 1011) at the 58 Pb/Zn smelting sites might pose adverse health effects and high chronic risks to children, respectively. The Pb/Zn smelting sites in the southwest and southeast provinces of China, as well as Liaoning province, were most contaminated and thus should receive priority for remediation.

A review of multi-risk methodologies for natural hazards: Consequences and challenges for a climate change impact assessment

This paper presents a review of existing multi-risk assessment concepts and tools applied by organisations and projects providing the basis for the development of a multi-risk methodology in a climate change perspective. Relevant initiatives were developed for the assessment of multiple natural hazards (e.g. floods, storm surges, droughts) affecting the same area in a defined timeframe (e.g. year, season, decade). Major research efforts were focused on the identification and aggregation of multiple hazard types (e.g. independent, correlated, cascading hazards) by means of quantitative and semi-quantitative approaches. Moreover, several methodologies aim to assess the vulnerability of multiple targets to specific natural hazards by means of vulnerability functions and indicators at the regional and local scale. The overall results of the review show that multi-risk approaches do not consider the effects of climate change and mostly rely on the analysis of static vulnerability (i.e. no time-dependent vulnerabilities, no changes among exposed elements). A relevant challenge is therefore to develop comprehensive formal approaches for the assessment of different climate-induced hazards and risks, including dynamic exposure and vulnerability. This requires the selection and aggregation of suitable hazard and vulnerability metrics to make a synthesis of information about multiple climate impacts, the spatial analysis and ranking of risks, including their visualization and communication to end-users. To face these issues, climate impact assessors should develop cross-sectorial collaborations among different expertise (e.g. modellers, natural scientists, economists) integrating information on climate change scenarios with sectorial climate impact assessment, towards the development of a comprehensive multi-risk assessment process.

Climate change impacts on marine water quality: The case study of the Northern Adriatic sea

Climate change is posing additional pressures on coastal ecosystems due to variations in water biogeochemical and physico-chemical parameters (e.g., pH, salinity) leading to aquatic ecosystem degradation. With the main aim of analyzing the potential impacts of climate change on marine water quality, a Regional Risk Assessment methodology was developed and applied to coastal marine waters of the North Adriatic. It integrates the outputs of regional biogeochemical and physico-chemical models considering future climate change scenarios (i.e., years 2070 and 2100) with site-specific environmental and socio-economic indicators. Results showed that salinity and temperature will be the main drivers of changes, together with macronutrients, especially in the area of the Po' river delta. The final outputs are exposure, susceptibility and risk maps supporting the communication of the potential consequences of climate change on water quality to decision makers and stakeholders and provide a basis for the definition of adaptation and management strategies.

Timbre Brownfield Prioritization Tool to support effective brownfield regeneration

In the last decade, the regeneration of derelict or underused sites, fully or partly located in urban areas (or so called "brownfields"), has become more common, since free developable land (or so called "greenfields") has more and more become a scare and, hence, more expensive resource, especially in densely populated areas. Although the regeneration of brownfield sites can offer development potentials, the complexity of these sites requires considerable efforts to successfully complete their revitalization projects and the proper selection of promising sites is a pre-requisite to efficiently allocate the limited financial resources. The identification and analysis of success factors for brownfield sites regeneration can support investors and decision makers in selecting those sites which are the most advantageous for successful regeneration. The objective of this paper is to present the Timbre Brownfield Prioritization Tool (TBPT), developed as a web-based solution to assist stakeholders responsible for wider territories or clusters of brownfield sites (portfolios) to identify which brownfield sites should be preferably considered for redevelopment or further investigation. The prioritization approach is based on a set of success factors properly identified through a systematic stakeholder engagement procedure. Within the TBPT these success factors are integrated by means of a Multi Criteria Decision Analysis (MCDA) methodology, which includes stakeholders' requalification objectives and perspectives related to the brownfield regeneration process and takes into account the three pillars of sustainability (economic, social and environmental dimensions). The tool has been applied to the South Moravia case study (Czech Republic), considering two different requalification objectives identified by local stakeholders, namely the selection of suitable locations for the development of a shopping centre and a solar power plant, respectively. The application of the TBPT to the case study showed that it is flexible and easy to adapt to different local contexts, allowing the assessors to introduce locally relevant parameters identified according to their expertise and considering the availability of local data.

Regional Risk Assessment for climate change impacts on coastal aquifers

Coastal aquifers have been identified as particularly vulnerable to impacts on water quantity and quality due to the high density of socio-economic activities and human assets in coastal regions and to the projected rising sea levels, contributing to the process of saltwater intrusion. This paper proposes a Regional Risk Assessment (RRA) methodology integrated with a chain of numerical models to evaluate potential climate change-related impacts on coastal aquifers and linked natural and human systems (i.e., wells, river, agricultural areas, lakes, forests and semi-natural environments). The RRA methodology employs Multi Criteria Decision Analysis methods and Geographic Information Systems functionalities to integrate heterogeneous spatial data on hazard, susceptibility and risk for saltwater intrusion and groundwater level variation. The proposed approach was applied on the Esino River basin (Italy) using future climate hazard scenarios based on a chain of climate, hydrological, hydraulic and groundwater system models running at different spatial scales. Models were forced with the IPCC SRES A1B emission scenario for the period 2071-2100 over four seasons (i.e., winter, spring, summer and autumn). Results indicate that in future seasons, climate change will cause few impacts on the lower Esino River valley. Groundwater level decrease will have limited effects: agricultural areas, forests and semi-natural environments will be at risk only in a region close to the coastline which covers less than 5% of the total surface of the considered receptors; less than 3.5% of the wells will be exposed in the worst scenario. Saltwater intrusion impact in future scenarios will be restricted to a narrow region close to the coastline (only few hundred meters), and thus it is expected to have very limited effects on the Esino coastal aquifer with no consequences on the considered natural and human systems.

A Multi-Criteria Decision Analysis based methodology for quantitatively scoring the reliability and relevance of ecotoxicological data

Ecotoxicological data are highly important for risk assessment processes and are used for deriving environmental quality criteria, which are enacted for assuring the good quality of waters, soils or sediments and achieving desirable environmental quality objectives. Therefore, it is of significant importance the evaluation of the reliability of available data for analysing their possible use in the aforementioned processes. The thorough analysis of currently available frameworks for the assessment of ecotoxicological data has led to the identification of significant flaws but at the same time various opportunities for improvement. In this context, a new methodology, based on Multi-Criteria Decision Analysis (MCDA) techniques, has been developed with the aim of analysing the reliability and relevance of ecotoxicological data (which are produced through laboratory biotests for individual effects), in a transparent quantitative way, through the use of expert knowledge, multiple criteria and fuzzy logic. The proposed methodology can be used for the production of weighted Species Sensitivity Weighted Distributions (SSWD), as a component of the ecological risk assessment of chemicals in aquatic systems. The MCDA aggregation methodology is described in detail and demonstrated through examples in the article and the hierarchically structured framework that is used for the evaluation and classification of ecotoxicological data is shortly discussed. The methodology is demonstrated for the aquatic compartment but it can be easily tailored to other environmental compartments (soil, air, sediments).

Species sensitivity weighted distribution for ecological risk assessment of engineered nanomaterials: the n-TiO2 case study

Societal concerns about the environmental risks of engineered nanomaterials (ENMs) have recently increased, but nano-ecological risk assessments are constrained by significant gaps in basic information on long-term effects and exposures, for example. An approach to the ecological risk assessment of ENMs is proposed that can operate in the context of high uncertainty. This approach further develops species sensitivity weighted distribution (SSWD) by including 3 weighting criteria (species relevance, trophic level abundance, and nanotoxicity data quality) to address nano-specific needs (n-SSWD). The application of n-SSWD is illustrated for nanoscale titanium dioxide (n-TiO2 ), which is available in different crystal forms; it was selected because of its widespread use in consumer products (e.g., cosmetics) and the ample availability of data from ecotoxicological studies in the literature (including endpoints for algae, invertebrates, bacteria, and vertebrates in freshwater, saltwater, and terrestrial compartments). The n-SSWD application resulted in estimation of environmental quality criteria (hazard concentration affecting 5% and 50% of the species) and ecological risk (potentially affected fraction of species), which were then compared with similar results obtained by applying the traditional species sensitivity distribution (SSD) approach to the same dataset. The n-SSWDs were also built for specific trophic levels (e.g., primary producers) and taxonomic groups (e.g., algae), which helped to identify the most sensitive organisms. These results showd that n-SSWD is a valuable risk tool, although further testing is suggested.

Ecological status classification of the Taizi River Basin, China: a comparison of integrated risk assessment approaches

Integrated risk assessment approaches allow to achieve a sound evaluation of ecological status of river basins and to gain knowledge about the likely causes of impairment, useful for informing and supporting the decision-making process. In this paper, the integrated risk assessment (IRA) methodology developed in the EU MODELKEY project (and implemented in the MODELKEY Decision Support System) is applied to the Taizi River (China), in order to assess its Ecological and Chemical Status according to EU Water Framework Directive (WFD) requirements. The available dataset is derived by an extensive survey carried out in 2009 and 2010 across the Taizi River catchment, including the monitoring of physico-chemical (i.e. DO, EC, NH3-_N, chemical oxygen demand (COD), biological oxygen demand in 5 days (BOD5) and TP), chemical (i.e. polycyclic aromatic hydrocarbons (PAHs) and metals), biological (i.e. macroinvertebrates, fish, and algae), and hydromorphological parameters (i.e. water quantity, channel change and morphology diversity). The results show a negative trend in the ecological status from the highland to the lowland of the Taizi River Basin. Organic pollution from agriculture and domestic sources (i.e. COD and BOD5), unstable hydrological regime (i.e. water quantity shortage) and chemical pollutants from industry (i.e. PAHs and metals) are found to be the main stressors impacting the ecological status of the Taizi River Basin. The comparison between the results of the IRA methodology and those of a previous study (Leigh et al. 2012) indicates that the selection of indicators and integrating methodologies can have a relevant impact on the classification of the ecological status. The IRA methodology, which integrates information from five lines of evidence (i.e., biology, physico-chemistry, chemistry, ecotoxicology and hydromorphology) required by WFD, allows to better identify the biological communities that are potentially at risk and the stressors that are most likely responsible for the observed alterations. This knowledge can be beneficial for a more effective restoration and management of the river basin ecosystem.

Risk-based prioritization methodology for the classification of groundwater pollution sources

Water management is one of the EU environmental priorities and it is one of the most serious challenges that today's major cities are facing. The main European regulation for the protection of water resources is represented by the Water Framework Directive (WFD) and the Groundwater Directive (2006/118/EC) which require the identification, risk-based ranking and management of sources of pollution and the identification of those contamination sources that threaten the achievement of groundwater's good quality status. The aim of this paper is to present a new risk-based prioritization methodology to support the determination of a management strategy for the achievement of the good quality status of groundwater. The proposed methodology encompasses the following steps: 1) hazard analysis, 2) pathway analysis, 3) receptor vulnerability analysis and 4) relative risk estimation. Moreover, by integrating GIS functionalities and Multi Criteria Decision Analysis (MCDA) techniques, it allows to: i) deal with several sources and multiple impacted receptors within the area of concern; ii) identify different receptors' vulnerability levels according to specific groundwater uses; iii) assess the risks posed by all contamination sources in the area; and iv) provide a risk-based ranking of the contamination sources that can threaten the achievement of the groundwater good quality status. The application of the proposed framework to a well-known industrialized area located in the surroundings of Milan (Italy) is illustrated in order to demonstrate the effectiveness of the proposed framework in supporting the identification of intervention priorities. Among the 32 sources analyzed in the case study, three sources received the highest relevance score, due to the medium-high relative risks estimated for Chromium (VI) and Perchloroethylene. The case study application showed that the developed methodology is flexible and easy to adapt to different contexts, thanks to the possibility to introduce specific relevant parameters identified according to expert judgment and data availability.

Multivariate interactions of natural and anthropogenic factors on Cd behavior in arable soil

The effect of one natural factor was generally weakened by binary interactions, which were further weakened by ternary interactions. This significant effect was computed via design of experiments.

Regional risk assessment approaches to land planning for industrial polluted areas in China: the Hulunbeier region case study

The rapid industrial development and urbanization processes that occurred in China over the past 30years has increased dramatically the consumption of natural resources and raw materials, thus exacerbating the human pressure on environmental ecosystems. In result, large scale environmental pollution of soil, natural waters and urban air were recorded. The development of effective industrial planning to support regional sustainable economy development has become an issue of serious concern for local authorities which need to select safe sites for new industrial settlements (i.e. industrial plants) according to assessment approaches considering cumulative impacts, synergistic pollution effects and risks of accidental releases. In order to support decision makers in the development of efficient and effective regional land-use plans encompassing the identification of suitable areas for new industrial settlements and areas in need of intervention measures, this study provides a spatial regional risk assessment methodology which integrates relative risk assessment (RRA) and socio-economic assessment (SEA) and makes use of spatial analysis (GIS) methodologies and multicriteria decision analysis (MCDA) techniques. The proposed methodology was applied to the Chinese region of Hulunbeier which is located in eastern Inner Mongolia Autonomous Region, adjacent to the Republic of Mongolia. The application results demonstrated the effectiveness of the proposed methodology in the identification of the most hazardous and risky industrial settlements, the most vulnerable regional receptors and the regional districts which resulted to be the most relevant for intervention measures since they are characterized by high regional risk and excellent socio-economic development conditions.

A risk-based methodology for ranking environmental chemical stressors at the regional scale

A "Risk-based Tool for the Regional Ranking of Environmental Chemical Stressors" has been developed, aimed at supporting decision-makers in the identification of priority environmental contaminants, as well as priority areas, to be further assessed. The tool implements a methodology based on a quantitative Weight-of-Evidence approach, integrating three types of information, identified as "Lines-of-Evidence" (LoE), namely: LoE "Environmental Contamination" (including data on chemical contamination in environmental matrices in the region, thus providing information on potential population exposure), LoE "Intake" (including results from human biomonitoring studies, i.e. concentration of chemicals in human biological matrices, thus providing an integrated estimation of exposure) and LoE "Observed Effects" (including information on the incidence of adverse health outcomes associated with environmental exposure to chemicals). A Multi-Criteria Decision Analysis (MCDA) methodology based on fuzzy logic has been developed to support the integration of information related to these three LoEs for each chemical stressor. The tool allows one to rank chemical stressors at different spatial scales, such as at the regional level as well as within each sub-area (e.g., counties). Moreover, it supports the identification of priority sub-areas within the region, where environmental and health data suggest possible adverse health effects and thus more investigation efforts are needed. To evaluate the performance of this newly developed tool, a case-study in the Flemish region (north of Belgium) has been selected. In the case-study, data on soil contamination by metals and organic contaminants were integrated with data on exposure and effect biomarkers measured in adolescents within the framework of the human biomonitoring study performed by the Flemish Centre of Expertise on Environment and Health in the period 2002-2006. The case-study demonstrated the performance of the tool in integrating qualitative and quantitative data with expert judgement for the identification of priority contaminants and areas. The proposed approach proved to be flexible, allowing for the incorporation of individual decision-maker's preferences, and, at the same time, to be transparent since all assumptions and value attributions are traceable.

Comparison of risk-based decision-support systems for brownfield site rehabilitation: DESYRE and SADA applied to a Romanian case study

Brownfield rehabilitation is an essential step for sustainable land-use planning and management in the European Union. In brownfield regeneration processes, the legacy contamination plays a significant role, firstly because of the persistent contaminants in soil or groundwater which extends the existing hazards and risks well into the future; and secondly, problems from historical contamination are often more difficult to manage than contamination caused by new activities. Due to the complexity associated with the management of brownfield site rehabilitation, Decision Support Systems (DSSs) have been developed to support problem holders and stakeholders in the decision-making process encompassing all phases of the rehabilitation. This paper presents a comparative study between two DSSs, namely SADA (Spatial Analysis and Decision Assistance) and DESYRE (Decision Support System for the Requalification of Contaminated Sites), with the main objective of showing the benefits of using DSSs to introduce and process data and then to disseminate results to different stakeholders involved in the decision-making process. For this purpose, a former car manufacturing plant located in the Brasov area, Central Romania, contaminated chiefly by heavy metals and total petroleum hydrocarbons, has been selected as a case study to apply the two examined DSSs. Major results presented here concern the analysis of the functionalities of the two DSSs in order to identify similarities, differences and complementarities and, thus, to provide an indication of the most suitable integration options.

Application of a quantitative weight of evidence approach for ranking and prioritising occupational exposure scenarios for titanium dioxide and carbon nanomaterials

Substantial limitations and uncertainties hinder the exposure assessment of engineered nanomaterials (ENMs). The present deficit of reliable measurements and models will inevitably lead in the near term to qualitative and uncertain exposure estimations, which may fail to support adequate risk assessment and management. Therefore it is necessary to complement the current toolset with user-friendly methods for near-term nanosafety evaluation. This paper proposes an approach for relative exposure screening of ENMs. For the first time, an exposure model explicitly implements quantitative weight of evidence (WoE) methods and utilises expert judgement for filling data gaps in the available evidence-base. Application of the framework is illustrated for screening of exposure scenarios for nanoscale titanium dioxide, carbon nanotubes and fullerenes, but it is applicable to other nanomaterials as well. The results show that the WoE-based model overestimates exposure for scenarios where expert judgement was substantially used to fill data gaps, which suggests its conservative nature. In order to test how variations in input data influence the obtained results, probabilistic Monte Carlo sensitivity analysis was applied to demonstrate that the model performs in stable manner.

Climate change impact assessment on Veneto and Friuli Plain groundwater. Part I: an integrated modeling approach for hazard scenario construction

Climate change impacts on water resources, particularly groundwater, is a highly debated topic worldwide, triggering international attention and interest from both researchers and policy makers due to its relevant link with European water policy directives (e.g. 2000/60/EC and 2007/118/EC) and related environmental objectives. The understanding of long-term impacts of climate variability and change is therefore a key challenge in order to address effective protection measures and to implement sustainable management of water resources. This paper presents the modeling approach adopted within the Life+ project TRUST (Tool for Regional-scale assessment of groUndwater Storage improvement in adaptation to climaTe change) in order to provide climate change hazard scenarios for the shallow groundwater of high Veneto and Friuli Plain, Northern Italy. Given the aim to evaluate potential impacts on water quantity and quality (e.g. groundwater level variation, decrease of water availability for irrigation, variations of nitrate infiltration processes), the modeling approach integrated an ensemble of climate, hydrologic and hydrogeologic models running from the global to the regional scale. Global and regional climate models and downscaling techniques were used to make climate simulations for the reference period 1961-1990 and the projection period 2010-2100. The simulation of the recent climate was performed using observed radiative forcings, whereas the projections have been done prescribing the radiative forcings according to the IPCC A1B emission scenario. The climate simulations and the downscaling, then, provided the precipitation, temperatures and evapo-transpiration fields used for the impact analysis. Based on downscaled climate projections, 3 reference scenarios for the period 2071-2100 (i.e. the driest, the wettest and the mild year) were selected and used to run a regional geomorphoclimatic and hydrogeological model. The final output of the model ensemble produced information about the potential variations of the water balance components (e.g. river discharge, groundwater level and volume) due to climate change. Such projections were used to develop potential hazard scenarios for the case study area, to be further applied within climate change risk assessment studies for groundwater resources and associated ecosystems. This paper describes the models' chain and the methodological approach adopted in the TRUST project and analyzes the hazard scenarios produced in order to investigate climate change risks for the case study area.