Artificial Intelligence-Assisted Urban Multi-Scenario Environmental Exposure Assessment and Smart Health Risk Management

In an era of rapid urbanization and major demographic shifts, cities have become the geographic epicenter of resource consumption and chemical emissions. Urban environmental multimedia challenges—including contamination of indoor and outdoor air, water, and soil, as well as noise, light pollution, and extreme heat—are intensifying. These stressors contribute to chronic public health risks and increasing hazards through multiple exposure pathways, with impacts that vary across spatial scales and population groups. In 2021, environmental and occupational risk factors in the Global Burden of Disease (GBD) study were responsible for 18.9% (12.8 million) of global deaths and 14.4% of disability-adjusted life years (DALYs). Guided by the UN Sustainable Development Goals (SDGs), the focus of urban development is shifting from efficiency and technology alone toward prioritizing citizen health and equity. Meanwhile, the rising frequency and intensity of epidemics and environmentally attributable disease outbreaks underscore the urgent need for intelligent, real-time, multi-scenario environmental exposure surveillance. Such advances are vital to understanding environmental health impacts, chronic disease etiology, health burdens, and inequities.Amid rapid advances in artificial intelligence (AI), the Internet of Things (IoT), smart wearables, and big data, smart multi-target environmental exposure management offers new opportunities to enable “4R” environmental risk governance: Right Time, Right Place, Right Information, Right Person. Integrating smart technologies into environmental monitoring, simulation, and assessment can improve our ability to collect, analyze, and respond to dynamic environmental data—supporting real-time regulation, predictive management, open-source tools, citizen sensing, and remote health monitoring. This evolution represents a critical step from “Big Data” to “Big Information.” Despite this promise, implementation challenges remain, including technical limitations and interoperability, input–output efficiency, the accuracy of multi-scenario simulations, usability barriers across user groups, privacy and security risks, health equity considerations, and the limited availability of market-ready applications and software.This Research Topic aims to provide a platform for interdisciplinary researchers advancing AI-assisted urban multi-scenario environmental exposure assessment and smart health risk management applications.Article typesWe welcome Original Research, Systematic Reviews, Methods, Mini Reviews, Policy and Practice Reviews, Perspectives, Case Reports, and Data Reports.Topics should include (but not limited to):• AI-assisted spatiotemporal environment and health data mining• Multimedia fate modeling of urban environmental contaminants• Portable, IoT-enabled environmental and exposure monitoring devices• Bioindicator identification for human exposure and toxicology• Environmental health exposure modeling and assessment• Spatiotemporal environmental and health economic evaluation• Data mining of population exposure behaviors across multiple scenarios• Multi-user intelligent health risk management systems and software• Environmental health equity and health economics Topic editors: Fei Li, Chengzhi Chen, Si Chen Source: https://www.frontiersin.org/research-topics/78655/artificial-intelligence-assisted-urban-multi-scenario-environmental-exposure-assessment-and-smart-health-risk-management