Early Life Ambient Air Pollution and Child Neurodevelopment: Critical Review of Cohort Evidence

Abstract

Background: Ambient air pollution is a pervasive exposure during pregnancy and childhood, when the central nervous system undergoes rapid development. Objective: To critically examine selected cohort evidence on prenatal and early-childhood exposure to PM2.5, PM10 and nitrogen dioxide (NO2), with particular attention to null findings, exposure assessment, confounding and causal interpretation. Methods: This narrative review used purposive evidence selection and targeted updating of PubMed-indexed records and publisher reports through 30 August 2026. Eligibility and selection priorities are stated explicitly. Representative studies were appraised qualitatively for exposure measurement, outcome ascertainment, confounding, selection, and multiplicity; no registered systematic review protocol, duplicate screening, formal risk-of-bias score, or de novo meta-analysis was used. Results: European and United States multicohort studies reported small associations with selected psychomotor, behavioural, and cognitive outcomes alongside substantial null findings. Canadian and South Korean cohorts further illustrate pollutant- and age-specific heterogeneity. Associations involving birth weight or prematurity require distinction between total and conditional effects. Placental, neuroimaging, and biomarker studies support biological plausibility but do not establish mediation or causation. A Chinese power-plant closure comparison provides complementary evidence, although concurrent temporal changes remain an alternative explanation. Conclusions: Early-life ambient air pollution may contribute to population-level neurodevelopmental risk, but the magnitude, pollutant specificity, and causal interpretation remain uncertain. The evidence does not establish pollution as a determinative cause of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), or an individual child’s developmental profile. More geographically inclusive cohorts and complementary causal designs are required.