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This study estimated the effects of the COVID-19 pandemic on maternal mortality in Chile between 2020 and 2021. A natural experiment was conducted using official data on maternal deaths and live births (LBs) between 1997 and 2021. The effects of the SARS-CoV-2 outbreak were evaluated using interrupted time series (ITS) and an autoregressive integrated moving average (ARIMA) model to forecast the expected rates on MMR and 95% confidence intervals (95% CI). In Chile, following World Health Organization suggestions, maternal deaths aggravated by SARS-CoV-2 are assigned to code O98.5 (non-respiratory infectious indirect) accompanied by code U07.1 or U07.2, depending on confirmation of the presence or absence of the virus. ITS analysis revealed that the SARS-CoV-2 outbreak impacted the MMR due to indirect causes, with a greater increase in indirect nonrespiratory causes than respiratory causes. The ARIMA forecast was consistent with ITS, showing that the expected MMR for indirect causes (3.44 in 2020 and 1.55 in 2021) was substantially lower than the observed rates (9.65 in 2020 and 7.46/100.000 LBs in 2021). For nonrespiratory indirect causes, the observed values of the MMR for 2020 (8.77/100.000 LBs) and 2021 (7.46/100.000 LBs) were double the predicted values of 4.02 (95% CI: 0.44-7.61) and 3.83 (95% CI: -0.12-7.79), respectively. A lower effect was observed on direct obstetrical deaths. During 2020-2021, there was a rise in the MMR in Chile attributable to SARS-CoV-2. The pandemic contributed to an escalation in the MMR due to indirect causes, particularly nonrespiratory and infectious causes. MMR due to direct obstetric causes were less affected. This suggests that the pandemic disproportionately affected maternal health by exacerbating conditions unrelated to pregnancy, childbirth, or postpartum, more than those directly linked to obstetric complications.

Greenhouse gas emissions, such as N2O from excessive N-fertilizer use, are of concern. Symbiotic N2-fixation by pulses such as soybean might mitigate this issue, for which inoculants carrying locally adapted Bradyrhizobium strains are recommended. In the frame of this goal, enhancing the quality control of these inoculants is required on two key aspects: determining the presence of nosZ, for the strains being able to reduce N2O, and assessing N2-fixation potential. Previously it was demonstrated that, in soybean leaves, N-contents are well correlated with chlorophyll contents. However, no such correlations were made with either N obtained from N2-fixation or with nodules mass, which is an indicator of nodular activity. Here we aimed to leverage the correlation between N and chlorophyll levels to develop a simple and non-destructive laboratory method to be applied in quality control of inoculants, able to assess the N2-fixing capacity of rhizobial strains. To establish such correlations, we cultivated soybeans in vermiculite with N-free nutrient solution, and inoculated them with various Bradyrhizobium field isolates that displayed a range of symbiotic N2-fixing capacities. Subsequently, we measured chlorophyll with a portable chlorophyllometer, and correlated these measures with symbiotic parameters. Moreover, we tested for the presence of nosZ by PCR. We observed significant correlations between chlorophyll and shoot nitrogen obtained from N2-fixation and, in addition, we corroborated that chlorophyll contents were significantly correlated also with nodules mass. Two B. diazoefficiens strains stood out and possessed nosZ. In contrast, B. elkanii and B. japonicum isolates displayed lower chlorophyll and symbiotic performance, and lacked nosZ. Our findings highlight the potential of measuring chlorophyll contents and testing for the presence of nosZ as two straightforward techniques that may enhance laboratory tests for quality control, enabling selection of superior and safe locally isolated strains for soybean inoculants without increased production costs.

Climate change presents a significant challenge to lagoon ecosystems, which are highly valued coastal environments known for their provision of unique ecosystem services. As important as fragile, lagoons are vulnerable to both natural processes and anthropogenic activities, and this vulnerability is exacerbated by the impacts of climate change, which are likely to result in severe ecological consequences. The complexity of water quality (WQ) processes, characterized by compounding and interconnected pressures, highlights the importance of adequate sophisticated methods to estimate future ecological impacts on lagoon environments. In this setting, a hybrid framework is introduced where Machine Learning (ML) and biogeochemical (BGC) models are integrated in a sequential modelling approach. This integration exploits the unique strengths offered by both models. The ML model allows capturing and learning linear and nonlinear correlations from historical data; the BGC interprets and simulates complex environmental systems subject to compounded pressures, building on identified causal relationships. Multi-Layer Perceptron (MLP) and Random Forest (RF) ML algorithms are trained, validated and tested within the Venice lagoon case study to assimilate historical WQ data (i.e., water temperature, salinity, and dissolved oxygen) and spatio-temporal information (i.e., monitoring station location and month), and to predict changes in chlorophyll-a (Chl-a) conditions. Then, projections from the BGC model SHYFEM-BFM for 2019, 2050, and 2100 timeframes under RCP 8.5 are integrated into the ML model (composing the hybrid ML-BGC model) to evaluate Chl-a variations under future biogeochemical conditions forced by climate change projections. Moreover, the SHYFEM-BFM standalone Chl-a projections are also used to compare the hybrid and the BGC scenarios. Annual and seasonal Chl-a predictions are developed by classes based on two classification modes (median and quartiles) established on the descriptive statistics computed on historical data. Results from the case study showed as the RF successfully classifies Chl-a with an overall model accuracy of about 80% for the median and 61% for the quartiles modes. Concerning future climate change scenarios, results revealed a decreasing trend for the lowest Chl-a values (below the first quartile, i.e. 0.85 µg/l) moving to the far future (2100), with an opposite rising trend for the highest Chl-a values (above the fourth quartile, i.e. 2.78 µg/l). On the seasonal level, summer remains the season with the highest Chl-a values in all scenarios, although in 2100 a strong increase in higher Chl-a values is also expected during the springtime one. The proposed hybrid framework represents a valuable approach to strengthen both multivariate Chl-a modelling and scenarios analysis, by placing artificial intelligence-based models alongside biogeochemical models.

The coastal environment is vulnerable to natural hazards and human-induced stressors. The assessment and management of coastal risks have become a challenging task, due to many environmental and socio-economic risk factors together with the complex interactions that might arise through natural and human-induced pressures. This work evaluates the combined effect of climate-related stressors on low-lying coastal areas by applying a multi-risk scenario analysis through a Bayesian Network (BN) approach for the Venice coast. Based on the available open-source and remote sensing data for detecting shoreline changes, the developed BN model was trained and validated with oceanographic variables for the 2015–2019 timeframe, allowing us to understand the dynamics of local-scale shoreline erosion and related water quality parameters. Three “what-if” scenarios were carried out to analyze the relationships between oceanographic boundary conditions, shoreline evolution, and water quality parameters. The results demonstrate that changes in sea surface height and significant wave height may significantly increase the probability of high-erosion and high-accretion states. Moreover, by altering the wave direction, the water quality variables show significant changes in the higher-risk class. The outcome of this study allowed us to identify current and future coastal risk scenarios, supporting local authorities in developing adaptation plans.

Whilst the last decades have seen a clear shift in emphasis from managing natural hazards to managing risk, the majority of natural-hazard risk research still focuses on single hazards. Internationally, there are calls for more attention for multi-hazards and multi-risks. Within the European Union (EU), the concepts of multi-hazard and multi-risk assessment and management have taken centre stage in recent years. In this perspective paper, we outline several key developments in multi-(hazard-)risk research in the last decade, with a particular focus on the EU. We present challenges for multi-(hazard-)risk management as outlined in several research projects and papers. We then present a research agenda for addressing these challenges. We argue for an approach that addresses multi-(hazard-)risk management through the lens of sustainability challenges that cut across sectors, regions, and hazards. In this approach, the starting point is a specific sustainability challenge, rather than an individual hazard or sector, and trade-offs and synergies are examined across sectors, regions, and hazards. We argue for in-depth case studies in which various approaches for multi-(hazard-)risk management are co-developed and tested in practice. Finally, we present a new pan-European research project in which our proposed research agenda will be implemented, with the goal of enabling stakeholders to develop forward-looking disaster risk management pathways that assess trade-offs and synergies of various strategies across sectors, hazards, and spatial scales.

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. [ABSTRACT FROM AUTHOR]

Water management in mountain regions is facing multiple pressures due to climate change and anthropogenic activities. This is particularly relevant for mountain areas where water abundance in the past allowed for many anthropogenic activities, exposing them to future water scarcity. Here stochastic system dynamics modelling (SDM) was implemented to explore water scarcity conditions affecting the stored water and turbined outflows in the Santa Giustina (S. Giustina) reservoir (Autonomous Province of Trento, Italy). The analysis relies on a model chain integrating outputs from climate change simulations into a hydrological model, the output of which was used to test and select statistical models in an SDM for replicating turbined water and stored volume within the S. Giustina dam reservoir. The study aims at simulating future conditions of the S. Giustina reservoir in terms of outflow and volume as well as implementing a set of metrics to analyse volume extreme conditions. Average results on 30-year slices of simulations show that even under the short-term RCP4.5 scenario (2021–2050) future reductions for stored volume and turbined outflow are expected to be severe compared to the 14-year baseline (1999–2004 and 2009–2016; −24.9 % of turbined outflow and −19.9 % of stored volume). Similar reductions are expected also for the long-term RCP8.5 scenario (2041–2070; −26.2 % of turbined outflow and −20.8 % of stored volume), mainly driven by the projected precipitations having a similar but lower trend especially in the last part of the 2041–2070 period. At a monthly level, stored volume and turbined outflow are expected to increase for December to March (outflow only), January to April (volume only) depending on scenarios and up to +32.5 % of stored volume in March for RCP8.5 for 2021–2050. Reductions are persistently occurring for the rest of the year from April to November for turbined outflows (down to −56.3 % in August) and from May to December for stored volume (down to −44.1 % in June). Metrics of frequency, duration and severity of future stored volume values suggest a general increase in terms of low volume below the 10th and 20th percentiles and a decrease of high-volume conditions above the 80th and 90th percentiles. These results point at higher percentage increases in frequency and severity for values below the 10th percentile, while volume values below the 20th percentile are expected to last longer. Above the 90th percentile, values are expected to be less frequent than baseline conditions, while showing smaller severity reductions compared to values above the 80th percentile. These results call for the adoption of adaptation strategies focusing on water demand reductions. Months of expected increases in water availability should be considered periods for water accumulation while preparing for potential persistent reductions of stored water and turbined outflows. This study provides results and methodological insights that can be used for future SDM upscaling to integrate different strategic mountain socio-economic sectors (e.g. hydropower, agriculture and tourism) and prepare for potential multi-risk conditions.

Background: Emerging pandemic viruses may have multiple deleterious effects on maternal health. This study examines the effects of a pandemic influenza virus on cause-specific maternal mortality time series, using Argentinian vital statistics. Methods: We conducted a population-based natural experiment from national vital records of maternal deaths between 1980 and 2017. Joinpoint regression models were used to model time series of the maternal mortality ratio (MMR). The sensitivity of the registry to detect the effects of the pandemic H1N1 2009 influenza virus on cause-specific MMR was analysed using a panel of parallel interrupted time series (ITS). Findings: Over this 38-year study, the MMR decreased by 58·6% (69·5 to 28·8 deaths/100,000 live births), transitioning from direct obstetric causes (67·0 to 21·1/100,000 live births; 68·4% decrease) to indirect causes (2·6 to 7·7/100,000 live births; 196·2% increase). The regression analysis showed an average reduction of -2·2%/year (95% CI: -2·9 to -1·4) with 2 join points in the total trend (1998 and 2009). Parallel ITS analyses revealed the pandemic H1N1 virus had an increasing effect on mortality from the respiratory system- and sepsis-related complications (level change 4·7 and 1·6/100,000 live births respectively), reversing after the outbreak. No effect was found on MMR from hypertensive disorders, haemorrhage, abortive outcomes, other direct obstetric causes, and indirect non-respiratory comorbidities. Interpretation: The Argentinian maternal death registry appears sensitive to detect different effects of emerging infectious epidemics on maternal health. In a population-based natural experiment, pandemic H1N1 virus impacted maternal mortality almost exclusively from the respiratory system- and sepsis-related complications. Funding: Supported by FISAR www.fisarchile.org

Extreme weather and climate related events, from river flooding to droughts and tropical cyclones, are likely to become both more severe and more frequent in the coming decades, and the damages caused by these events will be felt across all sectors of society. In the face of this threat, policy- and decision-makers are increasingly calling for new approaches and tools to support risk management and climate adaptation pathways that can capture the full extent of the impacts. In this frame, a GIS-based Bayesian Network (BN) approach is presented for the capturing and modelling of multi-sectoral flooding damages against future ‘what-if’ scenarios. Building on a risk-based conceptual framework, the BN model was trained and validated by exploiting data collected from the 2014 Secchia River flooding event, as well as other contextual variables. Moreover, a novel approach to defining the structure of the BN was performed, reconfiguring the model according to expert judgment and data-based validation. The model showed a good predictive capacity for damages in the agricultural, industrial and residential sectors, predicting the severity of damages with a classification accuracy of about 60% for each of these assessment endpoints. ‘What-if’ scenario analysis was performed to understand the potential impacts of future changes in i) land use patterns and ii) increasing flood depths resulting from more severe flood events. The output of the model showed a rising probability of experiencing high monetary damages under both scenarios. In spite of constraints within the case study dataset, the results of the appraisal show good promise, and together with the designed BN model itself represent a valuable support for disaster risk management and reduction actions against extreme river flooding events, enabling better informed decision making.

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). Key Points: The environmental management cycles for chemicals and climate change framework (EMC4) visualizes the commonalities of existing frameworks to communicate the intersections of risk assessment, risk management, and implementation.Pairing the framework with our eight guiding questions supports decision‐making stakeholders' problem‐scoping and management options for chemical risks influenced by climate change.The explicit inclusion of interface actors and implementation actors (i.e., the doers) in the framework as decision‐making stakeholders emphasizes that actions are needed for management, not just decisions.We are trying to manage a dynamic, open system and no one framework or policy will be the silver bullet; rather, a scaled and coordinated effort is needed to understand and explain the relationship between chemicals and climate change on a global scale down to the local scale. [ABSTRACT FROM AUTHOR]

The main goal of this work is to expand the availability of chiral columns for the analysis of agrochemicals by gas chromatography. A broader offer of chiral stationary phases would allow shifting toward enantioselective analytical techniques environmentally more friendly for those compounds. We prepared seven chiral capillary columns based on derivatives of either, β‐cyclodextrin or γ‐cyclodextrins dissolved at high concentrations, in two typical polysiloxanes with different polarities, demonstrating not only the significance of the chiral selector but also of the polymer solvent for achieving adequate enantioseparation of some agrochemicals. The enantiorecognition ability of each column was evaluated with 20 volatile and semivolatile agrochemicals, possessing one or two chiral centers. Besides, to elute more polar agrochemicals, as well as to enhance enantioselectivity, three derivatization procedures targeting the carboxyl and/or amine group were evaluated. The results revealed that the prepared column consisting of octakis(2,3‐di‐O‐acetyl‐6‐O‐tertbutyldimethylsilyl)‐γ‐cyclodextrin dissolved in (14%‐cyanopropyl‐phenyl)‐86%‐methyl‐polysiloxane provides the broadest enantiorecognition capacity. This column allowed the enantioseparation of seventeen chiral agrochemicals, including metalaxyl, furalaxyl, and four imidazolinones, which were not enantioseparated in the remaining columns. To the best of our knowledge, glufosinate, fluorochloridone, fenarimol, furalaxyl, and four imidazolinones were enantioseparated by gas chromatography for the first time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]