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A portion of Colombia’s water resources is located on the Pacific coast within the territory of the Community Council of Alto and Medio Dagua (CC-AMDA). Though a harmonious balance between the communities’ subsistent activities and nature was maintained for centuries, the appearance of modern modes of resource extraction has negatively affected the environment, especially the water resources. The Driver-Pressure-State- Impact-Response (DPSIR) framework was used to analyze water quality problems within this community council. The DPSIR analysis revealed that agriculture, mining, logging and infrastructure development constitute important sectoral drivers with some contribution from tourism and fisheries. Pressures included inputs of organic matter, sediment, nutrients and chemical contaminants to the Dagua river, and to the Bay of Buenaventura. These produced corresponding State changes in the water bodies. Impacts on human welfare were poor public health, reduced food and water security, economic loss and some displacement. Societal Responses included public protests and campaigns, legal actions and policy changes for improved governance. As a future policy option, the formation of community-based water resources management is recommended. Though DPSIR was able to link cause-effect relations, further empirical research on these water bodies is necessary to fill in existing gaps in the data set, particularly for public health threatening contaminants.
2018
Promoting healthy lifestyle behaviors such as physical activity, sleep, diet quality, stress management, hydration, and healthy habits requires adaptive systems capable of responding dynamically to changing behavioral and environmental conditions. However, the development and evaluation of personalized recommendation systems are challenged by fragmented observational data, privacy constraints, delayed feedback, and ethical limitations associated with long-term human experimentation. To address these challenges, this study proposes a digital twin-driven reinforcement learning framework for generating personalized behavioral recommendations in a fully simulated and statistically validated environment. The proposed framework formulates personalized behavioral recommendation as a stochastic Markov Decision Process (MDP) incorporating adherence uncertainty, behavioral drift, environmental modulation, and engagement dynamics. Synthetic longitudinal behavioral trajectories are generated through a digital twin simulator that models demographic heterogeneity, lifestyle behaviors, contextual variables, and variability in policy adherence over time. The optimization objective is defined through an effective reward formulation that balances behavioral compliance gains against penalties associated with health and environmental constraint violations. This study implements several reinforcement learning (RL) paradigms under simulated conditions, such as multi-armed bandits, table-based Q-learning, State-Action-Reward-State-Action (SARSA), function approximation-based temporal difference (TD) learning, and deep Q-learning network (DQN). The results demonstrate that richer state representations and context-dependent action dynamics are necessary for higher-capacity reinforcement learning models to consistently outperform simpler baselines. Furthermore, this study provides a reproducible method for comparing learning dynamics, performance, and computational cost in digital twin-based recommender systems. The framework additionally supports privacy-preserving experimentation through the exclusive use of synthetic behavioral data and locally controlled simulation environments.
2026
2015
2009
2016
Bisphenol A (BPA), a synthetic chemical widely used in the production of polycarbonate plastic and epoxy resins, has been associated with a variety of adverse effects in humans including metabolic, immunological, reproductive, and neurodevelopmental effects, raising concern about its health impact. In the EU, it has been classified as toxic to reproduction and as an endocrine disruptor and was thus included in the candidate list of substances of very high concern (SVHC). On this basis, its use has been banned or restricted in some products. As a consequence, industries turned to bisphenol alternatives, such as bisphenol S (BPS) and bisphenol F (BPF), which are now found in various consumer products, as well as in human matrices at a global scale. However, due to their toxicity, these two bisphenols are in the process of being regulated. Other BPA alternatives, whose potential toxicity remains largely unknown due to a knowledge gap, have also started to be used in manufacturing processes. The gradual restriction of the use of BPA underscores the importance of understanding the potential risks associated with its alternatives to avoid regrettable substitutions. This review aims to summarize the current knowledge on the potential hazards related to BPA alternatives prioritized by European Regulatory Agencies based on their regulatory relevance and selected to be studied under the European Partnership for the Assessment of Risks from Chemicals (PARC): BPE, BPAP, BPP, BPZ, BPS-MAE, and TCBPA. The focus is on data related to toxicokinetic, endocrine disruption, immunotoxicity, developmental neurotoxicity, and genotoxicity/carcinogenicity, which were considered the most relevant endpoints to assess the hazard related to those substances. The goal here is to identify the data gaps in BPA alternatives toxicology and hence formulate the future directions that will be taken in the frame of the PARC project, which seeks also to enhance chemical risk assessment methodologies using new approach methodologies (NAMs).
2024
2013
2013
2012
2012
2009
2010
2000
2024
2024
2014
2008
2000
A comprehensive survey of persistent organic pollutants in Norwegian birds of prey eggs. Organohalogen compounds, vol. 46
2000
A comprehensive quantification of global nitrous oxide sources and sinks
Nitrous oxide (N2O), like carbon dioxide, is a long-lived greenhouse gas that accumulates in the atmosphere. Over the past 150 years, increasing atmospheric N2O concentrations have contributed to stratospheric ozone depletion1 and climate change2, with the current rate of increase estimated at 2 per cent per decade. Existing national inventories do not provide a full picture of N2O emissions, owing to their omission of natural sources and limitations in methodology for attributing anthropogenic sources. Here we present a global N2O inventory that incorporates both natural and anthropogenic sources and accounts for the interaction between nitrogen additions and the biochemical processes that control N2O emissions. We use bottom-up (inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and top-down (atmospheric inversion) approaches to provide a comprehensive quantification of global N2O sources and sinks resulting from 21 natural and human sectors between 1980 and 2016. Global N2O emissions were 17.0 (minimum–maximum estimates: 12.2–23.5) teragrams of nitrogen per year (bottom-up) and 16.9 (15.9–17.7) teragrams of nitrogen per year (top-down) between 2007 and 2016. Global human-induced emissions, which are dominated by nitrogen additions to croplands, increased by 30% over the past four decades to 7.3 (4.2–11.4) teragrams of nitrogen per year. This increase was mainly responsible for the growth in the atmospheric burden. Our findings point to growing N2O emissions in emerging economies—particularly Brazil, China and India. Analysis of process-based model estimates reveals an emerging N2O–climate feedback resulting from interactions between nitrogen additions and climate change. The recent growth in N2O emissions exceeds some of the highest projected emission scenarios3,4, underscoring the urgency to mitigate N2O emissions.
2020
Using the example of sulfur hexafluoride (SF6), we investigate the use of Lagrangian particle dispersion models (LPDMs) for inverse modeling of greenhouse gas (GHG) emissions and explore the limitations of this approach. We put the main focus on the impacts of baseline methods and the LPDM backward simulation period on the a posteriori emissions determined by the inversion. We consider baseline methods that are based on a statistical selection of observations at individual measurement sites and a global-distribution-based (GDB) approach, where global mixing ratio fields are coupled to the LPDM back-trajectories at their termination points. We show that purely statistical baseline methods can cause large systematic errors, which lead to inversion results that are sensitive to the LPDM backward simulation period and can generate unrealistic global total a posteriori emissions. The GDB method produces a posteriori emissions that are far less sensitive to the backward simulation period and that show a better agreement with recognized global total emissions. Our results show that longer backward simulation periods, beyond the often used 5 to 10 d, reduce the mean squared error and increase the correlation between a priori modeled and observed mixing ratios. Also, the inversion becomes less sensitive to biases in the a priori emissions and the global mixing ratio fields for longer backward simulation periods. Further, longer periods might help to better constrain emissions in regions poorly covered by the global SF6 monitoring network. We find that the inclusion of existing flask measurements in the inversion helps to further close these gaps and suggest that a few additional and well-placed flask sampling sites would have great value for improving global a posteriori emission fields.
2022
2018
2011