Previous research has found an association between PM2.5 exposure and worsening depression; however, studies specifically examining the harmful effects of individual PM2.5 components are relatively limited. This national survey enrolled individuals aged 45 and older in mainland China, collecting personal data and assessing depression. Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10). Monthly exposure to PM2.5 and its seven components—black carbon (BC), organic matter (OM), nitrate (NO3−), sulfate (SO42−), ammonium (NH4+), soil particles (SOIL), and sea salt (SS)—was matched to each participant’s residence. Linear mixed-effects models (LMEs) assessed the association between single pollutants and depression score, while weighted quantile sum (WQS) regression examined the effect of mixed exposure and identified the contribution of each component. Modifying effects of social activity and green space were also evaluated. A total of 9725 participants were included. In single-exposure models, each interquartile range (IQR) increase in PM2.5 (29.18 μg/m3), BC (2.25 μg/m3), OM (7.18 μg/m3), SOIL (6.04 μg/m3), and SS (0.14 μg/m3) was significantly associated with an increase in depression score of 0.90 (95% CI: 0.59, 1.20), 0.71 (95% CI: 0.42, 1.09), 0.94 (95% CI: 0.61, 1.26), 0.51 (95% CI: 0.38, 0.63), and 0.53 (95% CI: 0.33, 0.73) points, respectively. In mixed-exposure models, each IQR increase in the mixture of all components was associated with a 1.104-point rise in depression score (95% CI: 0.901, 1.307), with BC having the largest weight (33.6%), followed by SOIL (28.59%) and SS (25.05%). The harmful effects of PM2.5 and specific components on depression were lower among those who participated in social activities or lived in areas with higher levels of green space (p < 0.05). These findings suggest that the harmful effects of PM2.5 on depression may be influenced by its components, and that social activity and green space could reduce the risk of depression associated with PM2.5 and its components.
Xiao et al. (Tue,) studied this question.
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