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Estimating the role of air quality improvements in the decline of suicide rates in China

Abstract

Emerging evidence suggests that air pollution may play a role in shaping suicide risk by altering brain function. However, this link is difficult to quantify and has yet to be investigated in China, where 16% of global suicides occur. Here we apply a statistical model that leverages random increases in particulate pollution (PM2.5) due to meteorological conditions to comprehensive data on suicide rates across Chinese counties. We find that a 1 s.d. (σ) increase in PM2.5 raises weekly suicide rates by 25%. This effect occurs without delay, consistent with neurobiological evidence that PM2.5 influences emotional regulation and impulsive–aggressive behaviour. Effects are sex and age specific; women over 65 exhibit significantly higher vulnerability. We estimate that PM2.5 reductions under China’s Air Pollution Action Plan prevented 13,000–79,000 (95% confidence interval) suicides over 2013–2017, accounting for 10% of this period’s observed suicide rate decline. Our findings uncover a causal link between particulate pollution and suicide, adding urgency to calls for pollution control policies across the globe.

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Fig. 1: Air pollution and suicide rates in China have declined substantially in recent years.
Fig. 2: Estimated effects of air pollution on suicide rates in China.
Fig. 3: Prevented suicides attributable to recent pollution declines.

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Data availability

Station-level air pollution data are publicly available through the CNEMC (http://www.cnemc.cn/en/). Cleaned and processed air pollution data used in this analysis are hosted publicly on Zenodo (https://doi.org/10.5281/zenodo.10433249). Weather data are provided under restricted access from the China Meteorological Data Service Center; researchers can apply for access to these data at https://data.cma.cn/data/cdcdetail/dataCode/A.0012.0001.S011.html. Suicide data are confidential and were obtained with permission directly from the CCDC. The basemaps used in Figs. 1b and 3a and Extended Data Fig. 1b are from https://www.resdc.cn/?aspxerrorpath=/DOI,2023.DOI:10.12078/2023010101; researchers can apply for access at the same link.

Code availability

All code necessary to replicate the analysis is provided in a public GitHub repository available at https://github.com/tcarleton/suicide-pm (ref. 62).

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Acknowledgements

We thank B. Sun, J. Rico-Straffon and T. Mangin for excellent research assistance. We thank J. Proctor and participants of the London School of Economics Environment Week conference, the University of Delaware School of Marine Science & Policy seminar series and the Bren School of Environmental Science & Management seminar at the University of California, Santa Barbara for comments and suggestions. P.Z. acknowledges support from the National Natural Science Foundation of China (grant number 72203193) and Natural Science Foundation of Guangdong Province, China (grant number 2023B1515020065).

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Contributions

P.Z. and T.C. conceptualized the study. P.Z., L.L. and M.Z. obtained and processed the data. P.Z. and T.C. conducted data analysis. T.C. designed and made display items. T.C. wrote the paper and P.Z. edited the paper.

Corresponding authors

Correspondence to Tamma Carleton, Liguo Lin or Maigeng Zhou.

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Extended data

Extended Data Fig. 1 Suicide data from the Chinese Center for Disease Control and Prevention Disease Surveillance Point (DSP) system.

a, Population-weighted average weekly suicide rate per 1 million population for males (black) and females (green) across weeks of the sample (‘w1’ indicates the first week of the year). Spring months (April, May, and June) are shaded in grey each year. b, Map of the 597 nationally representative counties for which suicide data are available through the DSP system. Basemap in b for 2015 from the Chinese Resource and Environmental Science Data Registration and Publishing System (https://www.resdc.cn/?aspxerrorpath=/DOI,2023.DOI:10.12078/2023010101).

Extended Data Table 1 Summary statistics
Extended Data Table 2 Impact of thermal inversions on PM2.5
Extended Data Table 3 Comparison of two-way fixed effects to instrumental variables
Extended Data Table 4 Robustness of main specification to definition of instrumental variable
Extended Data Table 5 Robustness of main specification to spatio-temporal controls
Extended Data Table 6 Robustness of main specification to weather controls
Extended Data Table 7 Robustness of main specification to clustering of standard errors
Extended Data Table 8 Lagged effects of thermal inversions on suicide rates

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2, Tables 1–3 and references.

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Zhang, P., Carleton, T., Lin, L. et al. Estimating the role of air quality improvements in the decline of suicide rates in China. Nat Sustain 7, 260–269 (2024). https://doi.org/10.1038/s41893-024-01281-2

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