Rural Airborne PM2.5-bound Microplastics and Heavy Metals: GIS-AI Risk Assessment Review

Abstract

Fine particulate matter (PM2.5) is a critical air pollutant and carrier of toxic co-contaminants, including microplastics (MPs) and heavy metals (HMs), posing severe human health risks. Rural exposure studies lag behind urban ones. This review systematically evaluates the sources, spatial and temporal patterns, analytical methods, and health risks of PM2.5-bound MP and HM across 110 articles (2008–2025) retrieved from the Web of Science database. The bibliometric analysis performed in the VoS viewer revealed sharp growth in publications post-2018, peaking in 2024. Major contributors are China (38.12%), the United States (15.45%), and India (10%), with the Chinese Academy of Sciences being the most productive institution. The keyword network identified HM, PM2.5, and MP as the dominant research themes. Rural concentrations of these PM2.5-bound contaminants vary widely due to seasonal factors, biomass burning, agriculture, and long-range transport. Although urban levels are often higher, rural risks may be equal to or exceed health risks observed in urban areas due to prolonged exposure and occupational hazards. Inhalation is identified as the primary exposure pathway, followed by ingestion and dermal contact. Health risks are commonly assessed using United States Environmental Protection Agency (USEPA) based cancer and non-cancer risk metrics, while Fourier Transform Infrared (FTIR), Raman spectroscopy, Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) remain the most widely applied analytical techniques. Existing studies exhibit considerable methodological inconsistency, limited integration of toxicology, and minimal use of Geographic Information Systems (GIS) and artificial intelligence (AI). The integration of GIS and AI approaches offers significant potential to improve source identification, spatial visualization and quantitative mapping, hotspot analysis, and predictive modeling, particularly in under-studied rural regions. These findings support the World Health Organization air quality guidelines and Sustainable Development Goals 3, 7, and 11. © 2026 Elsevier B.V.