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).

Sensitivity analysis for a participatory approach to enhance the climate resilience of Venice, Italy.

Increases in the magnitudes and frequencies of climate-related extreme events are redistributing risk across coastal systems, including their environmental, economic, and social components. Consequently, stakeholders (SHs) are faced with long-term challenges and complex information when managing assets, services, and uses of the coast. In this context, SH engagement is a key step for risk management and in the preparation of resilience plans to respond and adapt to climate change. This paper develops a participatory method to identify and prioritize a set of risk measures, combining multi-criteria analysis with sensitivity analysis. The process involved local and regional authorities of the Veneto region testing the method, including national, regional, and local government, catchment officers, research organizations, natural parks managers and Non-Governmental Organizations (NGOs). SHs identified and ranked a range of adaptation measures to increase climate resilience, with a focus on coastal risk in the Venice lagoon. Results demonstrate that the sensitivity analysis provides useful information on how different sectors of expertise can influence the ranking of the identified risk management measures, highlighting the value of investigating the preferences or priorities of different SH groups within the definition of adaptation plans.

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.

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.

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.

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.

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.

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 (BCFmetal-exposure concentration) and fish weight (Weightfish-Lengthfish) 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.

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), ß2-microglobulin (U-BMG), retinol binding protein (U-RBP), and N-acetyl-ß-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.