Editorial Perspective: Expanding the search for environmental exposures related to children's development

In the fields of child psychology and psychiatry, countless environmental factors have been identified as correlates of children's health and development, yet each of these generally accounts for a small proportion of variance. The unaccounted-for variation in a model may be due to measurement error, random variation, model misspecifications or unmeasured predictors. The search for predictors traditionally viewed as being quite distal in children's social ecology has rapidly expanded over the past decade. For example, concerns regarding the impact of climate change on planetary health have been accompanied by research directly assessing the impact of climate-related environmental stressors on the health and well-being of children and youth. In this space, factors such as ambient heat exposure and air pollution during sensitive windows of development have recently been incorporated into studies examining children's mental health and neurodevelopment. In this perspective paper, we discuss the results of two such papers published in the journal, one each focused on heat exposure (Cuartas et al., 2026) and air pollution (Oh & Song, 2026) in relation to children's neurodevelopmental outcomes. Drawing on large international samples and using diverse methodologies, these studies show several small but consistent associations with a range of child outcomes, thus providing compelling initial evidence of the effects of these climate-related environmental stressors on development. Still, many unanswered questions remain related to the generalizability and reliability of findings, the domains of development most impacted, and the specific factors that may either exacerbate or mitigate the effect of these stressors. With considerable practice and policy implications, we argue for continued work in this area using diverse samples, robust measurement tools, and integration of developmental theories that will move beyond simple associations to help capture the complex mechanisms underlying these associations.