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.

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.

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.

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.

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.

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.

Endocrine-disrupting chemicals (EDCs) disrupt the synthesis, transport, action, or metabolism of endogenous hormones in the human body. EDCs often enter the body through inhalation, ingestion, or dermal contact and can accumulate in the body. Remobilization or transfer of EDCs can occur during lactation, causing human milk to become contaminated with a variety of EDCs, which could expose nursing infants and children to these chemicals.

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.

Estimating the effect of environmental mixtures on survival outcomes is common in epidemiological studies, yet the applicability and performance of advanced mixture modeling methods in this context remains underexplored. In this review, we identify available methods for this context and evaluate their performance via simulations.

This scoping review aimed to map the scientific literature on mortality related to climate change and environmental manifestations in the Mediterranean region, explore the different methodological approaches used, identify research gaps, and suggest future research directions. This scoping review was conducted following the Joanna Briggs Institute (JBI) recommendations and the Preferred Reporting Items for Scoping Reviews (PRISMA-ScR). We included articles that examined the association between floods, wildfires, Saharan dust outbreaks, particulate matter (PM), urban heat islands (UHI), compound effects, and mortality.

In utero and childhood exposure to toxic metals is associated with poor child growth, a predictor of adverse health outcomes. Most existing research focuses on exposure to single metals; the effects of metal mixtures largely remain understudied. Further, few studies consider how diet/nutrients interact with metal mixtures.

Environmental noise has been identified as a risk factor for cardiovascular and metabolic outcomes. Its association with cancer risk remains poorly understood. This systematic review (on evidence up to February 2025) is to synthesize and meta-analyze the epidemiological evidence.

Environmental exposures have been linked to numerous health conditions. Personal sampling devices such as silicone wristbands have been deployed to better characterize these exposures. Few studies have investigated the use of silicone wristbands in low- and middle-income countries (LMICs). Understanding silicone wristbands in these settings is important since they have the potential to overcome certain logistical and cultural barriers that make biospecimen collection challenging. Thus, this scoping review aims to assess these studies by highlighting population, feasibility, accessibility, and ease of use.

In this article, we summarise recent developments, identify gaps, and propose a research agenda for quantitative health impact assessment (HIA) of environmental exposures linked to urban transport and land use. This is based on a workshop of 30 experts, complemented by targeted literature identified by participants to illustrate the state of research and practice gaps. The practice of quantitative HIA in urban transport and land use interventions covers a diverse range of methods, models, and frameworks. The selection of an appropriate model depends upon the use case, i.e., the research question, resources and expertise, and application. The plurality of models can be a strength if differences are explicit and their implications are understood. A major gap in most assessments and frameworks is the lack of equity consideration. This should be integrated into all stages of the HIA, considering exposures, susceptibility, disease burden, capacity to benefit, household budgets, responsibility for harm, and participation in the process. Scenarios of environmental exposures in urban transport and land use interventions are often overly simple, while the scenario design process of spatial planning is often opaque. Researchers should specify the involvement of stakeholders and the data, evidence, or behavioural model used to construct the scenario. Recent developments in exposure assessment (remote sensing and modelling) have increased the capacity to conduct HIAs for small geographies at scale. At the same time, advances in simulation have enabled the representation of behaviours at high spatial and temporal resolution. The combination can enable person-centric measures accounting for location, activities, and behaviours, with HIA proceeding ahead of epidemiology. Most HIAs still use Comparative Risk Assessment. This is suitable for estimating the disease burdens of environmental exposures, but more advanced longitudinal methods are better suited for studying interventions. Beyond health outcomes, well-being must be incorporated. The monetisation of health outcomes through welfare economics remains contentious. Representation of uncertainty is increasingly acknowledged. Value of Information methods can inform where new data collection would most efficiently reduce final result uncertainty. In the context of the climate crisis and related environmental limits, methods are needed that consider adaptation alongside mitigation and prevention and test robustness to an increasingly unstable future.

This review examines how equity-related issues are addressed in the frameworks and guidelines of True-Cost Accounting (TCA). It analyzes the methods used to measure and value (in)equity in the agrifood sector. It identifies gaps and shortcomings and offers preliminary suggestions to advance the integration of equity considerations in future applications of TCA.

Current high levels of meat production and consumption are linked to severe health and environmental issues around the world. One remedy to this situation is a shift towards more plant-based diets. Several factors influence consumers’ meat intake including personal, socio-cultural, and economic factors as well as characteristics of the food environment. An increasing number of intervention studies are addressing these factors to support the necessary diet shift. However, the relative impact of factors on diets as well as the effectiveness of interventions might differ across geographic settings. This literature review describes (i) how far extant intervention studies on meat consumption reduction take the regional context into account, and (ii) whether current findings allow to distill universal factors that effectively reduce meat consumption across regions and countries.

Geospatial analysis is an essential tool for research on the role of environmental exposures and health, and critical for understanding impacts of environmental risk factors on diseases with long latency (e.g. cardiovascular disease, dementia, cancers) as well as upstream behaviors including sleep, physical activity, and cognition. There is emerging interest in leveraging machine learning and artificial intelligence (AI) for environmental epidemiology research. In this review, we provide an accessible overview of recent advances.

Heat Health Warning Systems (HHWS) reduce heat-related morbidity and mortality. We reviewed scientific studies on HHWS that use meteorological variables, local climate-epidemiological evidence, personalization, and built environment factors to determine heat stress thresholds. We identified key factors to enhance their precision and effectiveness.

Many people in high-income countries obtain considerable portions of their diets from gardening, hunting, fishing, foraging, and raising animals. Yet food security research in these countries has focused on the roles of commercial and charitable food systems, ignoring non-market food self-provisioning. This review brings together existing evidence to build a holistic understanding of how home and wild food procurement (HWFP) interacts with various dimensions of food security in high-income societies.