Current Environmental Health Reports最新文献

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.

Purpose of review: Virtual reality (VR) has emerged as a novel approach to research built environmental determinants of physical activity for its ability to address issues of causality, which have historically plagued the discipline. The purpose of this narrative review is to identify the methods by which VR technology has been adapted for use within the research area.

Recent findings: Current built environmental VR research examining physical activity overwhelmingly targets walking and cycling. Despite spanning few types of PA, we observe diverse VR methodologies and patterns of applications within research areas. In this review, we explore how capabilities of current VR technology, specifically simulation development and travel, have shaped research questions, validity, and generalizability. We identify future innovations that may address these limitations. Finally, we encourage future research applying this powerful research tool to investigations of built environmental factors promoting types of physical activity apart from walking and cycling.

The rapid advancement of artificial intelligence (AI) presents unprecedented opportunities and challenges for assessing planetary health, particularly in environmental health. As a key determinant of human well-being, the environment significantly influences health. Although the application of AI in these areas has garnered increasing attention, a comprehensive evaluation framework is still lacking. In this review, we bridge this gap by proposing a unified evaluation framework that spans the entire environmental health research continuum, from modeling environmental exposures to assessing health outcomes and inferring causal relationships. We synthesize recent methodological innovations, application scenarios, and emerging trends across these interconnected domains. Our work highlights how AI can enhance accuracy, scalability, and causal understanding in environmental health studies. By emphasizing this integrated perspective, this review underscores AI's synergistic potential in addressing complex environmental health challenges and informing planetary health strategies.

Purpose of review: There are disparities in cancer incidence, mortality, and survival by race/ethnicity. As a result of structural mechanisms of discrimination, minoritized racial/ethnic groups are disproportionately exposed to higher levels of environmental carcinogens. Increased risk of exposure to harmful environmental pollutants may contribute to observed cancer disparities by race/ethnicity, but few studies have examined this effect. How race/ethnicity is operationalized in epidemiologic studies can impact interpretation of associations and potentially mask disparities, preventing the development of targeted public health interventions. We conducted a systematic review of epidemiologic studies on ambient environmental pollution and cancer outcomes in US adults and assessed how race/ethnicity was operationalized.

Recent findings: A total of 3,346 studies were identified. We found that of 172 studies that included race/ethnicity, 85/172 (49%) only considered race/ethnicity as a confounder. Of the remaining 87 studies, 60/87 (69%) stratified analyses by race/ethnicity, 9/87 (10%) were minority health studies that included one non-White racial/ethnic group, 18/87 (21%) examined estimated cancer risk as an outcome with race/ethnicity as the main exposure. Despite these limited analyses, many of these studies found stronger associations among racial/ethnic minority groups. One study examined environmental exposures as a causal mediator to explain potential disparities in cancer outcomes. There is a need for more research on racial/ethnic cancer disparities related to environmental pollutants. Researchers should consider developing data sources and leverage existing databases with robust racial/ethnic diversity and put ethical consideration into how race/ethnicity is included in conceptual frameworks to ensure fairness, equity, and clarity.

Purpose of review: Anaerobic manure digesters are a hotly debated and rapidly expanding technology that extracts biogas from animal manure. We assessed claims by proponents and opponents of the technology by reviewing evidence regarding digesters and pollutant emissions, occupational health, environmental injustice, economics, and climate.

Recent findings: Manure digesters can mitigate some impacts from industrial animal agriculture, such as odors and methane emissions, while potentially increasing or perpetuating others, such as ammonia emissions and nutrient pollution. While promoted as a climate solution, manure digesters only address a fraction of livestock-related greenhouse gas emissions and may exacerbate or introduce new occupational and community hazards, such as from flared biogas. Policies play a large role in subsidizing manure digesters, incentivizing further expansion of industrial animal agriculture-an industry with documented harms to rural populations. In summary, proponent claims in many cases overstated the evidence of actual benefits, while opponent concerns were either validated by the evidence or merit further investigation. Based on the current state of available evidence, manure digesters should not be promoted as a solution for manure management and energy production.