Current Environmental Health Reports最新文献

Purpose of review: The purpose of this short literature review is to present the state of art in One Health ethics, a new field working with ethics in One Health approaches. These approaches have focused on promoting health for humans, animals, plants, and ecosystems mainly in a scientific way. Ethics has often been left out. The goal is to summarize the main findings of the limited ethics discussion in the field and propose the future way forward for the field.

Recent findings: There have been several calls for ethics, and the main discussion has mainly focused on (1) the ethical imperative in One Health, (2) the ethical value of ecosystems, (3) normative aspects of health, (4) core ethical concepts, and (5) ethical decision models. For the next decade this field needs to be fully developed and included as a core science within the One Health approaches. To be able to solve the complex problems these approaches are facing, such as the triple crisis (climate change, pollution, and biodiversity loss), more scholars need to work with One Health ethics, which still is a rather underdeveloped field of ethics. Three future trends for the field of One Health ethics were proposed; 1) to find ethical decision models, 2) to bridge the gap between anthropocentrism, zoocentrism, biocentrism, and ecocentrism, and 3) how to balance valuation between different species, organisms or levels, or ethics and economy.

Background: Workplace heat exposure, intensified by climate change, increasingly threatens workers' health, safety, and productivity, especially in the agriculture, construction, and manufacturing sectors. However, current evidence is fragmented due to varied study designs, and the absence of an integrated, multidisciplinary synthesis.

Objectives: This umbrella review synthesizes findings from current systematic reviews and meta-analyses to appraise the health and productivity outcomes of workplace heat exposure, assess evidence quality, and identify critical research and policy gaps.

Methods: Fourteen systematic reviews and meta-analyses (published up to 31st March 2025) were included following predefined (PECOS) criteria. Methodological fidelity was analyzed using the AMSTAR checklist, and the strength of evidence was evaluated using the GRADE approach.

Results: The fidelity of the included reviews was rated from moderate to high, while the robustness of evidence spanned from low to moderate due to study heterogeneity and observational designs. Consistent evidence links workplace heat exposure to higher risks of heat-related illness, reduced eGFR (AOR = 3.50, 95% CI: 1.30-9.40) resulting renal impairment, cognitive decline, and injuries (1% increase in risk per 1℃ rises in temperature). Emerging findings suggests heat-induced sub-cellular and molecular damage (i.e., increased 8-OHdG, HSP70), reduced sperm quality, indicating cellular dysfunction. Women and relocated workers face greater physiological strain. Productivity losses affect 30-60% of exposed workers, with prior estimates suggesting annual global economic losses of approximately $2.1 trillion.

Conclusions: Workplace heat hazards significantly threaten global workforce health and economic resilience. Urgent, coordinated interventions, robust policy measures, and high-quality longitudinal research are required to alleviate these risks.

Purpose of review: Fine particulate matter (PM_2.5) poses a public health risk, disproportionately impacting low- and middle-income countries (LMICs). In Peru, where ambient concentrations in urban areas significantly exceed the World Health Organization's annual guideline of 5 µg/m³, lack of air pollution monitoring hinders exposure assessment, health effect research, and policy development. Here, we review efforts to create a national database of estimated ambient PM_2.5 in other LMICs, and then discuss our efforts in Peru.

Recent findings: We highlight the Peru-based NIH-funded GeoHealth Hub's efforts to establish a nationwide low-cost sensor (LCS) network of 176 PurpleAir monitors. We then describe a hybrid approach for modeling ambient PM_2.5 exposure across Peru, leveraging data from LCS, satellite remote sensing, chemical transport models, and advanced machine learning methods. The ground-monitoring network includes sensors in both urban (62.5%) and rural (37.5%) areas, in the 24 Regions of the country, set up in collaboration with national environmental agencies. Initial application of our hybrid approach in Lima demonstrated good prediction for the years 2010-2023, with an R² of 0.88 with existing regulatory ground monitors. We are working to extend the model across Peru at a daily level and at a 5-km² resolution for 2024-2026. The sustainability of these efforts will depend on building local capacity, securing long-term funding, and integrating the LCS network within the current regulatory environmental monitoring network. The hybrid approach offers a scalable solution to address data scarcity and enable high-resolution exposure modeling in Peru and other LMICs.

Purpose of review: Bangladesh frequently appears among the top five countries with the most polluted air. Research is essential to understand the various health impacts of air pollution in vulnerable populations. This review compiles evidence from January 2000 to May 2025 on the adverse health effects of air pollution among Bangladeshi women and children.

Recent findings: Long-term exposure mainly from biomass fuel burning leads to various health consequences in women, especially during pregnancy. Early life exposure also results in harmful health outcomes in children. Research on the effects of air pollution exposure in Bangladesh has primarily focused on adverse pregnancy or birth outcomes, chronic respiratory diseases and hypertension. There is limited information on childhood mortality, malnutrition, developmental disorders, and noncommunicable diseases such as cancer and mental illness, and occupational exposure-related outcomes. Further research is needed to establish a causal link between air pollution exposure and health impacts and inform interventions. Policies for air pollution mitigation require strict monitoring and enforcement by the government.

Purpose of review: The Water-Energy-Food (WEF) Nexus has become a crucial framework for understanding resource interdependencies, nevertheless its integration with health remains underexplored. This review paper examines the current literature in the Water Energy Food Health (WEFH) Nexus, emphasizing the complex relationships between resource security and public health outcomes. Through a systematic literature review, the existing research trends, methodological approaches, and regional disparities in WEFH studies are analyzed.

Recent findings: While health is an inherent component of resource systems, it is often treated as an externality rather than a central determinant. The review revealed that out of 1175 research articles screened, only 21 discussed WEFH as an integrated nexus. Hence, there is a crucial need for a comprehensive Water, Energy, Food, and Health research. The paper proposes a conceptual WEFH integration that positions health as both an outcome and a driver of sustainable resource management based on epidemiological evidence. By analyzing health metrics implicit to the WEF nexus, the study provides insights for researchers and policymakers seeking to develop holistic strategies for resilience and equity in resource governance.

Purpose of review: The integration of artificial intelligence (AI) into toxicology marks a profound paradigm shift in chemical safety science. No longer limited to automating traditional workflows, AI is redefining how we assess risk, interpret complex biological data, and inform regulatory decision-making. This article explores the convergence of AI and other new approach methodologies (NAMs), emphasizing key trends such as multimodal learning, causal inference, explainable AI (xAI), generative modeling, and federated learning.

Recent findings: These technologies enable more human-relevant, mechanistically grounded, and ethically aligned toxicological predictions-surpassing the reproducibility and scalability of animal-based methods. However, the dynamic nature of AI models challenges traditional validation paradigms. To address this, we introduced the e-validation framework, which operationalizes the TREAT principles (Trustworthiness, Reproducibility, Explainability, Applicability, Transparency) and incorporates AI-powered modules for reference chemical selection, virtual study simulation, mechanistic cross-validation, and post-validation surveillance through companion agents. Ethical considerations-including bias audits, equity audits, and participatory governance-are also foregrounded as critical elements for responsible AI adoption. The emergence of a co-pilot model, where AI augments but does not replace human judgment, offers a pragmatic path forward. Supported by evidence from the 2025 Stanford AI Index and recent regulatory advances, we argue that the infrastructure, economics, and policy momentum are now aligned for global-scale deployment of AI-based toxicology. The future of the field lies not in replicating legacy practices, but in reinventing toxicology as an adaptive, transparent, and ethically grounded science that delivers more accurate, inclusive, and human-centric safety assessments. Artificial intelligence (AI) is changing how we test chemicals for safety. Instead of using animals, new computer-based tools can predict how substances affect human health more quickly, accurately, and ethically. This article looks at how these technologies-like smart data systems, models that explain their reasoning, and even AI "agents" that run simulations-can improve toxicology. We also introduce a new idea called "e-validation", which uses AI to help validate these methods in real-time, not just once. This ensures the models stay up to date and reliable. But using AI safely means tackling big questions: Can we trust results we don't fully understand? How do we prevent unfairness or bias in the data? We suggest a "co-pilot" model, where AI supports, but doesn't replace, human experts. With better data sharing, strong ethics, and smarter oversight, AI can help make chemical safety testing more human-focused, fair, and effective.

Purpose of review: Interrupted time series (ITS) designs are increasingly used in environmental health to evaluate impacts of extreme weather events or policies. This paper aims to introduce traditional and contemporary ITS approaches, including machine learning algorithms and Bayesian frameworks, which enhance flexibility in modeling complex temporal patterns (e.g., seasonality and nonlinear trends) and spatially heterogeneous treatment effects. We present a comparative analysis of methods such as ARIMA, machine learning models, and Bayesian ITS, using a real-world case study: estimating excess respiratory hospitalizations during the 2018 wildfire smoke event in San Francisco.

Recent findings: Our study demonstrates the practical application of these methods and provides a guide for selecting and implementing ITS designs in environmental epidemiology. To ensure reproducibility, we share annotated datasets and R scripts, allowing researchers to replicate analyses and adapt workflows. While focused on environmental applications, particularly acute exposures like wildfire smoke, the framework is broadly applicable to public health interventions. This work advances ITS methodology by integrating contemporary statistical innovations and emphasizing actionable guidance for causal inference in complex, real-world settings.

Purpose of review: This scoping review characterizes research on consumer attitudes towards meat sustainability, with a focus on environmental impact and animal welfare, from peer-reviewed articles and gray literature sources published globally between 2010-2022.

Recent findings: Consumers are important levers of change for advancing meat sustainability. While more narrowly focused systematic reviews exist, consumer attitudes are complex and this area is methodologically diverse. Analysis of 512 peer-reviewed articles and 31 gray literature sources revealed growth in this area since 2010. Included studies spanned 65 countries, with more representation from higher-income countries, especially in earlier years. Consumer attitudes are multidimensional, which is reflected in the wide array of attitudinal constructs and methodological approaches in this literature. Most studies examined consumer attitudes towards multiple species or towards meat or livestock in general. While climate impacts were the most commonly studied specific sustainability consideration, lending some support to the "carbon tunnel vision" hypothesis, the most common approach was to study environmental impact or animal welfare only at a general level, which we characterize as a "blurred vision"-a lack of focus on specific sustainability considerations, and their complexity and tradeoffs. Peer-reviewed and gray literature sources offer complementary perspectives, with many gray literature sources leveraging large public opinion polls and many peer-reviewed articles studying the mechanisms behind these polls. We recommend engaging broadly with multiple methodological approaches, and with both peer-reviewed and gray literature. Advancing the sustainability of animal agriculture requires the exchange of research findings across multiple scientific disciplines and sectors.