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. 2025 Mar 31;20(3):e0320084.
doi: 10.1371/journal.pone.0320084. eCollection 2025.

The impact of fine particulate matter on depression: Evidence from social media in China

Yao Zhong  1 , Jianxin Guo  1 , Hongbiao Wang  1 , Zhufeng Qiao  1 , Jichun Zhao  1 , Lei Chen  1 , Ying Nie  1
Affiliations

The impact of fine particulate matter on depression: Evidence from social media in China

Yao Zhong et al. PLoS One. .

Abstract

Depression is a significant public health issue in China that imposes a heavy economic burden on society and families. Using a dataset of 8.54 million Weibo posts from 284 prefecture-level cities across China between 2016 and 2019, we calculate the depression tendency index for residents in each city. Using the weighting of pollutants in nearby cities as an instrumental variable, we apply the two-stage least squares method to estimate the impact of PM2.5 on depression. The findings reveal that (1) air pollution markedly influences residents' susceptibility to depression, and every 1 μg/m3 increase in the PM2.5 concentration results in a 0.0559% increase in the depression tendency value. (2) The influence of air pollution on residents' depression exhibits a distinct weekly pattern, with individuals in heating cities, on weekdays, and in lower-income brackets being more impacted. (3) Our analysis of healthcare expenditures affirms that China's environmental governance policies have yielded significant economic advantages. As mitigation strategies, we propose the adoption of air pollution evasion measures, persistent refinement and enforcement of air pollution regulatory policies to reduce environmental pollution-related damage, paying attention to groups at risk of depression and fostering a healthy society.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Distribution of 284 sample cities in China.
Each point represents a city.
Fig 2
Fig 2. Annual average PM2.5 differences in sample cities between 2016 and 2019.
According to the legend, each point represents a city, and the color ranges from sky-blue to orange. A color closer to orange indicates that the PM2.5 concentration in the city in 2019 was greater than that in 2016. A negative value indicates that the PM2.5 concentration decreased over the four years.
Fig 3
Fig 3. Annual average distributions of PM2.5 (3a) and total depressive tendency values (3b) in the sample cities from 2016 to 2019.
Each point represents a city. The annual average concentration distribution of PM2.5 in sample cities from 2016 to 2019, with the left legend indicating the range of the annual average concentration of PM2.5; the larger the value is, the greater the concentration of PM2.5, and one color represents the annual concentration range of PM2.5. If the average annual concentration of PM2.5 in a certain city is less than or equal to 25 μg/m3, the city is marked in sky-blue in Fig 3a, and if the annual average concentration of PM2.5 in the city is greater than or equal to 75 μg/m3, it is marked in orange. Fig 3b shows the average 4-year distribution of the total depressive tendency in the sample cities from 2016 to 2019. The numbers represent the total depressive tendency values, and different colors represent different ranges of the total depression tendency values. The larger the value is, the greater the depressive tendency.
Fig 4
Fig 4. Distribution of the estimated coefficients of the PM2.5 regression after random assignment.
See this image and copyright information in PMC

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